Automatic camera selection

Abstract

Systems and techniques are provided for selecting a camera from a plurality of cameras. For example, a process may include obtaining an image from a camera. In some cases, the process may include determining a depth of field (DOF) range associated with a scene captured in the image. In some aspects, the process may include comparing the DOF range associated with the scene captured in the image to one or more DOF ranges of a respective camera of a plurality of cameras. In some examples, the process may include determining a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the image to a respective one or more DOF ranges for each camera of the plurality of cameras.

Description

Technical Field

[0001] The present disclosure relates generally to capturing images, and more particularly to systems and techniques for selecting a camera from a plurality of available cameras. Background Art

[0002] Many devices and systems allow for capturing a scene by generating images (or frames) and / or video data (including multiple frames) of the scene. For example, a camera or a device including a camera can capture a sequence of images of a scene (e.g., a video of the scene). In some cases, the sequence of images can be processed for performing one or more functions, can be output for display, can be output for processing and / or consumption by other devices, and other uses.

[0003] Devices (e.g., mobile devices) and systems increasingly utilize dedicated ultra-low power camera hardware to enable "always on" (AON) camera use cases, where the camera can remain on to record continuously while maintaining a lower power usage footprint. An AON camera can capture an image or video of a scene before a user initiates the image or video capture. However, the total power consumption of an AON camera setup to capture an image or video may still significantly reduce the battery life of the mobile device, which typically has a limited battery life. In some cases, an AON camera setup can utilize low power camera hardware to reduce power consumption. Summary of the invention

[0004] A system, apparatus, method, and computer-readable medium for selecting a camera from a plurality of cameras are disclosed. According to at least one example, a method for selecting a camera from a plurality of cameras is provided. The method includes: obtaining an image from a camera; determining a depth of field (DOF) range associated with a scene captured in the image; comparing the DOF range associated with the scene captured in the image with one or more DOF ranges of corresponding cameras in a plurality of cameras; and determining a selected camera from a plurality of cameras based on comparing the DOF range associated with the scene captured in the image with the corresponding one or more DOF ranges of each of the plurality of cameras.

[0005] In another example, an apparatus for selecting a camera from a plurality of cameras is provided, the apparatus comprising at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to: obtain an image from a camera; determine a depth of field (DOF) range associated with a scene captured in the image; compare the DOF range associated with the scene captured in the image with one or more DOF ranges of a corresponding camera in the plurality of cameras; and determine a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the image with the corresponding one or more DOF ranges of each camera in the plurality of cameras.

[0006] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided that, when executed by one or more processors, causes the one or more processors to: obtain an image from a camera; determine a depth of field (DOF) range associated with a scene captured in the image; compare the DOF range associated with the scene captured in the image with one or more DOF ranges of corresponding cameras in a plurality of cameras; and determine a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the image with the corresponding one or more DOF ranges of each of the plurality of cameras.

[0007] In another example, an apparatus for selecting a camera from a plurality of cameras is provided. The apparatus includes: means for obtaining an image from a camera; means for determining a depth of field (DOF) range associated with a scene captured in the image; means for comparing the DOF range associated with the scene captured in the image with one or more DOF ranges of a corresponding camera in a plurality of cameras; and means for determining a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the image with the corresponding one or more DOF ranges of each of the plurality of cameras.

[0008] In some aspects, one or more of the above devices are or are part of a vehicle (e.g., a computing device of a vehicle), a mobile device (e.g., a mobile phone or so-called "smartphone" or other mobile device), a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a server computer, or other device. In some aspects, a device includes one or more cameras for capturing one or more images. In some aspects, the device also includes a display for displaying one or more images, notifications, and / or other displayable data. In some aspects, the device may include one or more sensors that can be used to determine the position and / or posture of the device, the state of the device, and / or for other purposes.

[0009] This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

[0010] The foregoing and other features and embodiments will become more apparent upon reference to the following description, claims and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Exemplary embodiments of the present application are described in detail below with reference to the following drawings:

[0012] Figure 1 is a block diagram illustrating the architecture of an image capture and processing system according to some examples;

[0013] Figure 2 is a block diagram illustrating an example image processing system according to some examples;

[0014] Figure 3A and Figure 3B is a diagram illustrating the relationship between camera configuration and depth of field (DOF) range according to some examples;

[0015] Figure 4 is a block diagram illustrating an example camera selection subsystem according to some examples;

[0016] Figure 5 is a flow chart illustrating an example of a process for selecting a camera from a plurality of available cameras according to some examples;

[0017] Figure 6 is a flow chart illustrating an example of a process for selecting a camera from a plurality of available cameras according to some examples;

[0018] Figure 7 is a block diagram illustrating an example of a deep learning network according to some examples;

[0019] Figure 8 is a block diagram illustrating an example of a convolutional neural network according to some examples;

[0020] Fig. 9 is a diagram illustrating an example of a computing system for implementing certain aspects described herein. DETAILED DESCRIPTION

[0021] Some aspects and embodiments of the present disclosure are provided below. Some of these aspects and embodiments can be applied independently, and some of them can be applied in combination, which is obvious to those skilled in the art. In the following description, specific details are set forth for explanation purposes in order to provide a thorough understanding of each embodiment of the application. However, it will be apparent that each embodiment can be put into practice without these specific details. Each drawing and description are not intended to be restrictive.

[0022] The following description provides only exemplary embodiments and is not intended to limit the scope, applicability or configuration of the present disclosure. On the contrary, the subsequent description of the exemplary embodiments will provide an enabling description for implementing the exemplary embodiments to those skilled in the art. It should be understood that various changes can be made to the function and arrangement of elements without departing from the essence and scope of the present application as set forth in the appended claims.

[0023] Systems and techniques for selecting a camera from a plurality of available cameras are described herein. In some examples, an image capture system may implement a lower power or "always on" (AON) camera that operates continuously or periodically to automatically detect certain objects in an environment. For example, an image capture system that can capture preliminary images with an AON camera may be useful in a situation where the image capture system includes a selection of multiple cameras for capturing images of a scene. For example, a device may include a telephoto camera, a wide-angle camera, and / or another camera. The device may automatically select a camera to be activated from the available cameras. In some cases, the selection may be based on a determined depth of field (DOF) range associated with the scene and one or more DOF ranges associated with each of the plurality of cameras. In an illustrative example, the DOF of a scene may be determined based on object recognition and depth estimation and / or measurement. For example, the determined DOF of a scene may be selected so that both the nearest identified subject and the farthest identified subject (e.g., a person, a friend, a pet, an object, a landmark, and / or any other subject of interest) will be captured with acceptable focus. In some cases, the selection may be based on a field of view (FOV) associated with a scene. For example, a camera may be selected from the available cameras based on having a FOV large enough to capture all identified subjects.

[0024] Although examples are described herein with reference to AON cameras, such aspects may be applied to any camera or image sensor operating in low power mode. In some cases, AON cameras may utilize low power camera hardware to reduce power consumption. In some cases, AON cameras may operate with low power settings and / or perform different or fewer image processing steps to reduce power consumption. The amount of power consumed by the AON camera for determining which camera to select from a plurality of available cameras may be less than the power required for each single camera and capturing images to determine which camera is associated with the DOF range that most closely matches the DOF of the scene. In some cases, after the user initiates video and / or image capture, the selected camera in the plurality of cameras may capture a static image or begin capturing a video frame. Compared to AON or other low power cameras, the selected camera may utilize higher power camera hardware and / or may operate with higher power settings and / or different or more image processing steps.

[0025] As used herein, a DOF range refers to a near distance of acceptable focus and a far distance of acceptable focus. A DOF range may have a corresponding DOF corresponding to a distance between a near distance of acceptable focus and a far distance of acceptable focus. In some cases, a single camera (e.g., one or more of a plurality of available cameras) may be associated with a DOF range. For example, a camera with a fixed aperture and a fixed lens position may have a fixed near distance and a far distance of acceptable focus. However, in some cases, a single camera may be associated with multiple DOF ranges. In an illustrative example, a single camera may include a variable aperture. In some cases, as the aperture of the variable aperture changes, the DOF of the camera (including the near depth of acceptable focus and / or the far distance of acceptable focus) may also change. In some examples, the DOF range of a camera may be expressed by a mathematical relationship (e.g., a function) that can be used to calculate the DOF range within a range of camera settings (e.g., aperture, lens position, etc.).

[0026] In an illustrative example, a single camera or sensor may be configured to operate in different operating modes (e.g., low-power AON mode and non-AON mode). References to "AON operation" herein may be understood to include capturing images and / or frames (e.g., video frames) using one or more AON cameras and / or one or more cameras operating in AON mode. In the present disclosure, images captured during AON operation are sometimes referred to as low-power images. Similarly, references to "standard operation" may be understood to include capturing images using one or more non-AON cameras and / or one or more cameras operating in non-AON mode. In addition, non-AON cameras or sensors and / or cameras or sensors operating in non-AON mode may sometimes be referred to as one or more "main cameras" or "main camera sensors". In the present disclosure, images captured during standard camera operation are sometimes referred to as high-power images. The system and technology will be described herein as being performed relative to a static image. However, it should be understood that the system and technology may operate using any image or frame sequence (such as, continuously captured static images, videos, etc.). It should be understood that although specific examples of the present disclosure are discussed in terms of an AON camera (or AON camera sensor) and a main camera (or main camera sensor), the systems and techniques described herein may be applied to many different camera or sensor configurations without departing from the scope of the present disclosure.

[0027] A user may initiate image and / or video capture by, for example, pressing a capture or record button (also referred to as a shutter button in some cases), performing a gesture, and / or any other method of providing capture input. In some cases, prior to pressing the capture or record button, the user may perform an action that may be recognized by the device as indicating that the user may be preparing to capture an image and / or video. In some cases, the systems and techniques may recognize that capture is likely to occur (e.g., once the user presses the capture or record button) and proactively begin the process of selecting a camera from a plurality of cameras to capture an image of the scene detected by the AON camera in the preliminary image.

[0028] In some cases, AON operation may include capturing images at a low resolution (e.g., a VGA resolution of 640 pixels by 480 pixels, a WVGA resolution of 800 pixels by 480 pixels, etc.). In some cases, standard operation may include capturing images at a higher resolution than AON operation (e.g., 720p, 1080i, 1080p, 4K, 8K, etc.). Capturing and processing images at a lower resolution during AON operation may consume less power than capturing and processing video data at a higher resolution during standard operation. For example, the amount of power required to read an image captured by an image capture system may increase as the number of pixels read out increases. In addition, the amount of power required for image post-processing of the captured image data may also increase as the number of pixels in the image increases. In some aspects, in the case of selecting a camera from a plurality of available cameras, power savings may increase as the number of cameras available for selection increases. For example, power savings may be calculated relative to the power consumption associated with starting each available camera, capturing images, and analyzing the images captured from each camera to determine a preferred camera for capturing a scene.

[0029] Although specific example configurations for selecting a camera from a plurality of available cameras are described herein, the systems and techniques described herein may be used to select between a plurality of cameras without departing from the scope of the present disclosure. For example, these systems and techniques may be used to select between a plurality of DOF ranges for a single camera. In such examples, a single camera may include a variable aperture, a movable lens, any other DOF adjustment, and / or any combination thereof.

[0030] Various aspects of the technology described herein will be discussed below with reference to the various figures. Figure 11 is a block diagram illustrating the architecture of an image capture and processing system 100. The image capture and processing system 100 includes various components for capturing and processing an image of a scene (e.g., an image of a scene 110). The image capture and processing system 100 can capture an independent image (or photograph) and / or can capture a video including multiple images (or video frames) in a particular sequence. A lens 115 of the image capture and processing system 100 faces the scene 110 and receives light from the scene 110. The lens 115 bends the light toward the image sensor 130. The light received by the lens 115 passes through an aperture controlled by one or more control mechanisms 120 and is received by the image sensor 130.

[0031] The one or more control mechanisms 120 may control exposure, focus, and / or zoom based on information from the image sensor 130 and / or based on information from the image processor 150. The one or more control mechanisms 120 may include a plurality of mechanisms and components; for example, the control mechanisms 120 may include one or more exposure control mechanisms 125A, one or more focus control mechanisms 125B, and / or one or more zoom control mechanisms 125C. The one or more control mechanisms 120 may also include additional control mechanisms in addition to those illustrated, such as control mechanisms that control analog gain, flash, HDR, DOF, and / or other image capture properties.

[0032] A focus control mechanism 125B of the control mechanism 120 may obtain a focus setting. In some examples, the focus control mechanism 125B stores the focus setting in a memory register. Based on the focus setting, the focus control mechanism 125B may adjust the position of the lens 115 relative to the position of the image sensor 130. For example, based on the focus setting, the focus control mechanism 125B may adjust the focus by actuating a motor or servo system (or other lens mechanism) to move the lens 115 closer to the image sensor 130 or further away from the image sensor 130. In some cases, an additional lens may be included in the image capture and processing system 100, such as one or more micro lenses located above each photodiode of the image sensor 130, each of which bends the light received from the lens 115 toward the corresponding photodiode before the light reaches the corresponding photodiode. The focus setting may be determined via contrast detection autofocus (CDAF), phase detection autofocus (PDAF), hybrid autofocus (HAF), or some combination thereof. The focus setting may be determined using the control mechanism 120, the image sensor 130, and / or the image processor 150. Focus settings may be referred to as image capture settings and / or image processing settings.

[0033] Exposure control mechanism 125A of control mechanism 120 may obtain an exposure setting. In some cases, exposure control mechanism 125A stores the exposure setting in a memory register. Based on the exposure setting, exposure control mechanism 125A may control the size of the aperture (e.g., aperture size or f / stop), the duration that the aperture is open (e.g., exposure time or shutter speed), the sensitivity of image sensor 130 (e.g., ISO speed or film speed), the analog gain applied by image sensor 130, or any combination thereof. The exposure setting may be referred to as an image capture setting and / or an image processing setting.

[0034] A zoom control mechanism 125C of the control mechanism 120 may obtain a zoom setting. In some examples, the zoom control mechanism 125C stores the zoom setting in a memory register. Based on the zoom setting, the zoom control mechanism 125C may control the focal length of a lens element assembly (lens assembly) including the lens 115 and one or more additional lenses. For example, the zoom control mechanism 125C may control the focal length of the lens assembly by actuating one or more motors or servos (or other lens mechanisms) to move one or more lenses relative to each other. The zoom setting may be referred to as an image capture setting and / or an image processing setting. In some examples, the lens assembly may include a parfocal zoom lens or a variable focal length zoom lens. In some examples, the lens assembly may include a focusing lens (in some cases, the focusing lens may be the lens 115) that first receives light from the scene 110, wherein the light then passes through an afocal zoom system between the focusing lens (e.g., the lens 115) and the image sensor 130 before the light reaches the image sensor 130. In some cases, the afocal zoom system can include two positive (e.g., converging, convex) lenses with equal or similar focal lengths (e.g., within a threshold difference of each other), with a negative (e.g., diverging, concave) lens between them. In some cases, zoom control mechanism 125C moves one or more of the lenses in the afocal zoom system, such as the negative lens and one or both of the positive lenses.

[0035] Image sensor 130 includes one or more arrays of photodiodes or other photosensitive elements. Each photodiode measures the amount of light that ultimately corresponds to a particular pixel in the image generated by image sensor 130. In some cases, different photodiodes may be covered by different color filters, and thus may measure light that matches the color of the color filter covering the photodiode. For example, a Bayer color filter includes a red filter, a blue filter, and a green filter, where each pixel of an image is generated based on red light data from at least one photodiode covered in a red filter, blue light data from at least one photodiode covered in a blue filter, and green light data from at least one photodiode covered in a green filter. Other types of color filters may use yellow, magenta, and / or cyan (also referred to as "emerald") color filters instead of or in addition to red, blue, and / or green filters. Some image sensors (e.g., image sensor 130) may have no color filters at all, and may instead use different photodiodes (vertically stacked in some cases) throughout the pixel array. Different photodiodes throughout the pixel array may have different spectral sensitivity curves, thereby responding to different wavelengths of light.Monochrome image sensors may also lack color filters, and therefore lack color depth.

[0036] In some cases, image sensor 130 may alternatively or additionally include an opaque and / or reflective mask that blocks light from reaching certain photodiodes or portions of certain photodiodes at certain times and / or from certain angles, which may be used for phase detection autofocus (PDAF). Image sensor 130 may also include an analog gain amplifier to amplify the analog signal output by the photodiode and / or an analog-to-digital converter (ADC) to convert the analog signal output by the photodiode (and / or the analog signal amplified by the analog gain amplifier) ​​into a digital signal. In some cases, certain components or functions discussed with respect to one or more of control mechanisms 120 may alternatively or additionally be included in image sensor 130. Image sensor 130 may be a charge coupled device (CCD) sensor, an electron multiplying CCD (EMCCD) sensor, an active pixel sensor (APS), a complementary metal oxide semiconductor (CMOS), an N-type metal oxide semiconductor (NMOS), a hybrid CCD / CMOS sensor (e.g., sCMOS), or some other combination thereof.

[0037] The image processor 150 may include one or more processors, such as one or more image signal processors (ISPs) (including ISP 154), one or more host processors (including host processor 152), and / or one or more of any other type of processors 910 discussed with respect to the computing system 900. The host processor 152 may be a digital signal processor (DSP) and / or other type of processor. In some implementations, the image processor 150 is a single integrated circuit or chip (e.g., referred to as a system on a chip or SoC) that includes the host processor 152 and the ISP 154. In some cases, the chip may also include one or more input / output ports (e.g., input / output (I / O) port 156), a central processing unit (CPU), a graphics processing unit (GPU), a broadband modem (e.g., 3G, 4G or LTE, 5G, etc.), memory, connectivity components (e.g., Bluetooth TM , global positioning system (GPS), etc.), any combination thereof, and / or other components. The I / O port 156 may include any suitable input / output port or interface according to one or more protocols or specifications, such as an Inter-Integrated Circuit 2 (I2C) interface, an Inter-Integrated Circuit 3 (I3C) interface, a Serial Peripheral Interface (SPI) interface, a serial general purpose input / output (GPIO) interface, a Mobile Industry Processor Interface (MIPI) (such as a MIPI CSI-2 physical (PHY) layer port or interface, an Advanced High-Performance Bus (AHB) bus, any combination thereof, and / or other input / output ports. In an illustrative example, the host processor 152 may communicate with the image sensor 130 using an I2C port, and the ISP 154 may communicate with the image sensor 130 using a MIPI port.

[0038] The image processor 150 may perform a number of tasks, such as demosaicing, color space conversion, image downsampling, pixel interpolation, automatic exposure (AE) control, automatic gain control (AGC), CDAF, PDAF, automatic white balance, merging images to form an HDR image, image recognition, object recognition, feature recognition, receiving input, managing output, managing memory, or some combination thereof. The image processor 150 may store images and / or processed images in a random access memory (RAM) 140 / 925, a read-only memory (ROM) 145 / 920, a cache, a memory unit, another storage device, or some combination thereof.

[0039] Various input / output (I / O) devices 160 may be connected to the image processor 150. The I / O devices 160 may include a display screen, a keyboard, a keypad, a touch screen, a touchpad, a touch-sensitive surface, a printer, any other output device 935, any other input device 945, or some combination thereof. In some cases, subtitles may be entered into the image processing device 105B through a physical keyboard or keypad of the I / O device 160, or through a virtual keyboard or keypad of a touch screen of the I / O device 160. The I / O 160 may include one or more ports, jacks, or other connectors that enable a wired connection between the image capture and processing system 100 and one or more peripheral devices, through which the image capture and processing system 100 can receive data from one or more peripheral devices and / or send data to one or more peripheral devices. The I / O 160 may include one or more wireless transceivers that enable a wireless connection between the image capture and processing system 100 and one or more peripheral devices, through which the image capture and processing system 100 can receive data from one or more peripheral devices and / or send data to one or more peripheral devices. Peripheral devices may include any of the types of I / O devices 160 discussed previously, and may themselves be considered I / O devices 160 once they are coupled to a port, jack, wireless transceiver, or other wired and / or wireless connector.

[0040] In some cases, the image capture and processing system 100 may be a single device. In some cases, the image capture and processing system 100 may be two or more separate devices, including an image capture device 105A (e.g., a camera) and an image processing device 105B (e.g., a computing device coupled to the camera). In some implementations, the image capture device 105A and the image processing device 105B may be coupled together, for example, via one or more wires, cables, or other electrical connectors, and / or wirelessly coupled together via one or more wireless transceivers. In some implementations, the image capture device 105A and the image processing device 105B may be disconnected from each other.

[0041] like Figure 1 As shown, the vertical dotted line will Figure 1 1 is divided into two parts, representing image capture device 105A and image processing device 105B. Image capture device 105A includes lens 115, control mechanism 120 and image sensor 130. Image processing device 105B includes image processor 150 (including ISP 154 and host processor 152), RAM 140, ROM 145 and I / O 160. In some cases, some components illustrated in image capture device 105A (such as ISP 154 and / or host processor 152) may be included in image capture device 105A.

[0042] The image capture and processing system 100 may include an electronic device, such as a mobile or fixed telephone handset (e.g., a smart phone, a cellular phone, etc.), a desktop computer, a laptop or notebook computer, a tablet computer, a set-top box, a television, a camera, a display device, a digital media player, a video game console, a video streaming device, an Internet Protocol (IP) camera, or any other suitable electronic device. In some examples, the image capture and processing system 100 may include one or more wireless transceivers for wireless communication, such as cellular network communication, 802.11 wi-fi communication, wireless local area network (WLAN) communication, or some combination thereof. In some specific implementations, the image capture device 105A and the image processing device 105B may be different devices. For example, the image capture device 105A may include a camera device, and the image processing device 105B may include a computing device, such as a mobile phone, a desktop computer, or other computing devices.

[0043] Although the image capture and processing system 100 is shown as including certain components, one of ordinary skill will appreciate that the image capture and processing system 100 may include more than Figure 1 . The components of the image capture and processing system 100 may include software, hardware, or one or more combinations of software and hardware. For example, in some implementations, the components of the image capture and processing system 100 may include electronic circuits or other electronic hardware, and / or may be implemented using electronic circuits or other electronic hardware, which may include one or more programmable electronic circuits (e.g., microprocessors, GPUs, DSPs, CPUs, and / or other suitable electronic circuits), and / or may include computer software, firmware, or any combination thereof, and / or may be implemented using computer software, firmware, or any combination thereof to perform the various operations described herein. The software and / or firmware may include one or more instructions stored on a computer-readable storage medium and executable by one or more processors of an electronic device that implements the image capture and processing system 100.

[0044] In some examples, Figure 2 The extended reality (XR) system 200 may include the image capture and processing system 100, the image capture device 105A, the image processing device 105B, or a combination thereof.

[0045] Figure 2 An example block diagram of an image processing system 200 is illustrated. In some examples, the image processing system 200 may include or be included in the image capture and processing system 100, the image capture device 105A, the image processing device 105B, portions thereof, or any combination thereof. Figure 2 In the illustrative example of , the image processing system 200 includes an AON camera processing subsystem 202, a plurality of main camera processing subsystems 204-1 to 204-M (collectively referred to herein as main camera processing subsystems 204), a graphics processing subsystem 206, a video processing subsystem 208, a central processing unit (CPU) 210, a DRAM subsystem 212, an AON camera sensor 216, a plurality of main camera sensors 218-1 to 218-M (collectively referred to herein as main camera sensors 218), one or more sensors 219, and an SRAM 220. The value M may be an integer corresponding to the total number of main camera processing subsystems 204 and / or main camera sensors 218.

[0046] In some cases, AON camera sensor 216 and / or main camera sensor 218 may provide different DOF ranges for capturing images based on associated optical characteristics.

[0047] refer to Figure 3A and Figure 3B , provides illustrative examples of DOF ranges associated with different camera configurations. Figure 3A In the illustration of FIG. 3 , a first configuration 300 of the camera and a corresponding DOF range 314 are shown. Figure 3A In the illustrated example of , the camera includes a lens assembly 304 and an image sensor 302. As illustrated, the image sensor 302 can be spaced apart from the lens assembly by a distance 306. In some cases, the distance 306 is determined by positioning the lens assembly 304 and the sensor 302 in a housing of an electronic device (not shown). The camera configuration 300 can have a corresponding focus distance 308 and a viewing angle ω and a corresponding FOV indicated by diverging lines 310 starting at the center of the lens assembly 304. The lens assembly 304 can include an aperture, and the size of the aperture is indicated by a light cone 312. The DOF range 314 of the first configuration 300 is shown in D 近 With D 远 Extends between, where D 近 is the close distance of acceptable sharpness for the first configuration 300, D 远 is the long distance of acceptable focus for the first configuration 300. The short distance D of acceptable focus 近 and long distance D 远 It may be determined according to the following equations (1) to (3) based on characteristics of an optical system including the sensor 302 and the lens assembly 304 (eg, one or more lenses, an aperture, etc.).

[0048]

[0049] Where H is the hyperfocal focus distance in millimeters (mm), s is the focus distance 308, f is the focal length in mm, N is the aperture number, and c is the circle of confusion in mm.

[0050] Figure 3B A second configuration 350 is illustrated in which the aperture of the lens assembly 304 has been reduced, as shown by the light cone 322. Figure 3B In the illustrated example of FIG. , an image sensor 302, a lens assembly 304, and a corresponding lens-to-sensor distance 306, a corresponding focus distance 308, and a viewing angle ω and Figure 3A However, due to the size of the aperture relative to Figure 3A As φ decreases (as shown by the narrower light cone 322), the resulting DOF range 324 increases. Figure 3B As shown, the DOF range 324 has a D 近 Compare Figure 3A DOF range 314D 近 Closer to the lens assembly 304. Figure 3B In the illustrated example, the DOF range of 324 is D 远 Compare Figure 3A DOF range 314D 远 Further away from the lens assembly 304 .

[0051] return Figure 2In some implementations, the AON camera processing subsystem 202 may receive input from the AON camera sensor 216, and each master camera processing subsystem 204 may receive input from a corresponding master camera sensor 218. For example, the master camera processing subsystem 204-1 is shown as receiving input from the master camera sensor 218-1, and the master camera processing subsystem 204-M is shown as receiving input from the master camera sensor 218-M. The AON camera sensor 216 and the master camera sensor 218 may include any image and / or video sensor or capture device. In some cases, the AON camera sensor 216 and the master camera sensor 218 may be part of a multi-camera assembly, such as a dual camera assembly. In some examples, the AON camera sensor 216 and the master camera sensor 218 may include the image capture and processing system 100, the image capture device 105A, the image processing device 105B, or a combination thereof. In some implementations, the AON camera processing subsystem 202 of the image processing system 200 can communicate with the AON camera sensor 216 to transmit operating parameters to and / or receive operating parameters from the AON camera sensor 216. Similarly, in some implementations, each of the main camera processing subsystems 204 of the image processing system 200 can communicate with a corresponding one of the main camera sensors 218 to transmit operating parameters to and / or receive operating parameters from the corresponding one of the main camera sensors 218. The DRAM subsystem 212 of the image processing system 200 can communicate with the DRAM 214 via the data bus 215. For example, the DRAM subsystem 212 can transmit images to and / or retrieve images from the DRAM 214. In some implementations, the image processing system 200 can include a local SRAM 220.

[0052] In some cases, the AON camera sensor 216 may include optimizations for reducing power consumption. In some cases, the AON camera processing subsystem 202 may be configured to store data (e.g., image data) in an SRAM 220 located within the image processing system 200. In some cases, storing data in the SRAM 220 can save power by reducing the power required to drive data and address lines when compared to driving signals via the data bus 215 to communicate with the DRAM 214. In some implementations, an island voltage rail may be used to power the AON camera sensor 216 and the AON camera processing subsystem 202. In some cases, using an island rail may save power by preventing inactive components of the image processing system 200 from drawing power. In some examples, the AON camera sensor 216 may be clocked with a low-power clock source such as one or more ring oscillators. In some implementations, scenes in images and / or videos captured by the AON camera sensor 216 may be associated with a DOF range. As described above, the DOF range may include a close distance of acceptable focus and a long distance of acceptable focus. In one illustrative example, AON camera sensor 216 can capture images at a lower resolution than main camera sensor 218. In some cases, capturing lower resolution images with AON camera sensor 216 can save power by reducing the amount of data (e.g., image pixel data) that needs to be read out from AON camera sensor 216. In some implementations, AON camera processing subsystem 202 can perform similar processing steps as main camera processing subsystem 204 on fewer pixels, resulting in fewer computations and, therefore, reduced power consumption.

[0053] In some implementations, images captured by the AON camera sensor 216 and the main camera sensor 218 may be processed using different image processing steps. For example, the AON camera processing subsystem 202 may perform fewer and / or different processing steps than some or all of the main camera processing subsystem 204. In some cases, performing fewer and / or different processing operations with the AON camera processing subsystem 202 may save power during AON operation.

[0054] In another example, the AON camera sensor 216 may capture a monochrome image, while the main camera sensor 218 may capture a red, green, blue (RGB) color image. In some cases, reading out and processing a monochrome image may consume less power than reading out and processing an RGB color image. In some cases, the images captured by the AON camera sensor 216 and the main camera sensor 218 may be based on data captured from different parts of the spectrum, such as visible, ultraviolet (UV), near infrared (NIR), short wave infrared (SWIR), other parts of the spectrum, or any combination thereof.

[0055] like Figure 2As illustrated, the image processing system 200 may include one or more other sensors 219. In some implementations, the sensors 219 may include one or more inertial measurement units (IMUs), accelerometers, gyroscopes, range sensors, light-based sensors, radio-based sensors, sound-based sensors, audio sensors, etc. For example, the sensors 219 may include a range sensor that may provide depth information corresponding to pixels in an image captured by the AON camera sensor 216 and / or the main camera sensor 218. Other components of the image processing system 200 may also receive data from the sensors 219, such as the camera selection subsystem 205 and / or the CPU 210.

[0056] In some cases, the camera selection subsystem 205 may receive input from the AON camera processing subsystem 202. In some cases, the camera selection subsystem 205 may receive input from the sensor 219 and / or the CPU 210. In some cases, the camera selection subsystem 205 may process the preliminary image obtained from the AON camera processing subsystem 202 to determine the DOF range associated with the scene captured in the preliminary image. In some examples, depth information corresponding to the preliminary image may be obtained from the sensor 219. In some implementations, depth data may be obtained from the AON camera sensor 216. For example, phase detection, etc. may be used to determine the depth information. In some cases, the camera selection subsystem 205 may compare the DOF range associated with the scene captured in the preliminary image with the DOF capability of the main camera sensor 218.

[0057] refer to Figure 4 , a block diagram of an example camera selection subsystem 405 is shown. The camera selection subsystem 405 may correspond to Figure 2 405. In the illustrated example, the camera selection subsystem 405 includes a scene analysis subsystem 430, a DOF comparison subsystem 440, and a camera selector 450. In some cases, the camera selection subsystem 405 may receive input from the AON camera processing subsystem 402 and the sensor 419. In the illustrated example, the AON camera processing subsystem 402 may correspond to Figure 2 AON camera processing subsystem 202. Similarly, sensor 419 may correspond to Figure 2 In some examples, the camera selection subsystem 405 may provide an output to control the camera sensor 418. Figure 4 In the illustrated example, the camera sensor 418 may correspond to Figure 2 The main camera sensor 218 is shown.

[0058] In some examples, scene analysis subsystem 430 may include subject recognition engine 432 and DOF processing engine 434. In some implementations, scene analysis subsystem 430 may be used to determine a DOF range associated with a scene captured in an image received from AON camera processing subsystem 402. For example, when camera sensor 418 is in an inactive or low power state, AON camera processing subsystem 402 may provide an image to camera selection subsystem 405. In some cases, an image received from AON camera processing subsystem 402 when camera sensor 418 is inactive may be referred to as a preliminary image.

[0059] In some cases, subject recognition engine 432 may include a machine learning engine (not shown) trained to extract features from an input image. In some cases, the machine learning engine included in subject recognition engine 432 may generate classifications based on data from AON camera processing subsystem 402 and / or sensor 419. In some cases, using the classifications generated by subject recognition engine 432, scene analysis subsystem 430 may determine that the preliminary image includes a subject of interest captured by one of camera sensors 418. For example, subject recognition engine 432 may determine that one or more people, pets, objects, scenery, buildings may be subjects of interest. In some cases, the subject recognition engine 432 may generate classifications based on data previously generated by subject recognition engine 432 including subject analysis subsystem 430. Figure 4 The subject of interest may be determined from an existing image captured by a device of the camera selection subsystem 405 of the camera selection subsystem 405. In some cases, the identified subject may be provided to the DOF processing engine 434 to determine a DOF range associated with the scene captured in the preliminary image. For example, the DOF range associated with the scene captured in the preliminary image may be determined such that the DOF range associated with the scene is 近 Approximately at the location of the nearest subject detected by subject recognition engine 432, and with a DOF range D associated with the scene 远 Approximately at the location of the farthest subject detected by subject recognition engine 432 .

[0060] In some implementations, subject recognition engine 432 may determine a FOV associated with a subject detected in a preliminary image. For example, subject recognition engine 432 may determine a FOV associated with a scene that is sufficient to capture a person, friend, pet, object, etc. identified in a captured image.

[0061] In some cases, the DOF processing engine 434 may utilize depth information obtained from the sensor 419 and / or the AON camera processing subsystem 402 to determine a DOF range associated with the scene based on the preliminary image. For example, the DOF processing engine 434 may obtain depth values ​​corresponding to pixel locations of subjects detected in the preliminary image. In some cases, the depth of field processing engine 434 may determine the closest distance and the farthest distance of the pixels corresponding to the detected subjects, and determine the DOF range associated with the scene based on the determined closest distance and the farthest distance.

[0062] In some cases, the DOF comparison subsystem 440 may compare the DOF range associated with the scene with the DOF range (e.g., DOF capabilities) of each of the camera sensors 418. In some cases, the DOF comparison subsystem 440 may determine which camera sensor 418 can provide a DOF range that includes the DOF range associated with the scene. In some cases, if only one of the camera sensors 418 can provide a DOF range that includes the DOF range associated with the scene, the camera selector 450 may select the relevant camera sensor 418. In some cases, two or more of the camera sensors 418 may be able to provide acceptable focus for the DOF range associated with the scene. In some examples, the DOF comparison subsystem 440 may provide a numerical correspondence between the DOF range associated with the scene and the DOF range provided by each of the camera sensors 418. In some cases, the DOF comparison subsystem 440 may determine one or more of the camera sensors 418 that can provide a DOF range that includes the DOF range associated with the scene. In some cases, the numerical correspondence may include a percentage value, ratio, proportion, function, or relationship. In one illustrative example, the numerical correspondence may be determined as a DOF score based on the following equation (4):

[0063]

[0064] Where DOF 分数 is the DOF fraction, DOF 场景 is the DOF associated with the scene (e.g., measured in meters), and DOF 传感器 is a DOF range that can be provided by a particular camera sensor in camera sensors 418. In some implementations, a DOF score can be calculated for each DOF range that can be provided by each camera sensor in camera sensors 418.

[0065] In some cases, DOF comparison subsystem 440 may calculate a single numerical correspondence for a particular one of camera sensors 418. In some cases, DOF comparison subsystem 440 may calculate multiple numerical correspondences for a particular one of camera sensors 418. For example, if the camera sensor includes a variable aperture and / or a movable lens element, the camera sensor may be able to provide multiple DOF ranges.

[0066] In some cases, FOV comparison subsystem 445 may compare the FOV associated with the scene determined by scene analysis subsystem 430 with the FOV capabilities of each of camera sensors 418. In some examples, FOV comparison subsystem 445 may provide a numerical correspondence between the FOV associated with the scene and the FOV provided by each of camera sensors 418, and may determine one or more of camera sensors 418 that can provide a FOV range that includes the FOV range associated with the scene. In some cases, the numerical correspondence may include a percentage value, ratio, proportion, function, or relationship. In one illustrative example, the numerical correspondence may be determined as a FOV score based on the following equation (5):

[0067]

[0068] FOV 分数 is the FOV score, FOV 场景 is the FOV associated with the scene, and FOV 传感器 is a FOV range that can be provided by a particular camera sensor in camera sensors 418. In some implementations, a FOV score can be calculated for each FOV range that can be provided by each camera sensor in camera sensors 418.

[0069] In some implementations, the camera selector 450 can select the camera sensor 418 having the highest DOF. 分数 As the camera sensor to be activated to capture an image of the scene. In some implementations, the camera selector 450 may select the camera sensor 418 having the highest FOV 分数 In some implementations, the camera selector 450 may be based on the DOF 分数 and FOV 分数In some cases, the camera selection subsystem 405 can reduce the power consumed by the electronic device by selecting the camera sensors 418 based on a preliminary image from a single sensor (e.g., an AON sensor) rather than activating each of the camera sensors 418 to capture a preliminary image and determining which camera sensor of the camera sensors 418 to select based on a comparison of the captured preliminary images.

[0070] return Figure 2 In some aspects, the DOF range of each of the primary camera sensors 218 may be stored in a memory (eg, Fig. 9 The DOF range may be in the DRAM 214, SRAM 220, system memory 915, or cache memory 912 of the main camera sensor 218, or any other suitable memory). In some cases, one or more of the main camera sensors 218 may have a single associated DOF range. For example, for a camera sensor with a fixed aperture and a fixed lens, there may be only a single associated DOF range.

[0071] exist Figure 2 In the example of FIG. 2 , the AON camera sensor 216 and the main camera sensor 218 may each provide images to different camera processing subsystems in a one-to-one (1:1) relationship. However, in some cases, a single camera processing subsystem may process images (or frames) from multiple camera sensors including the AON camera sensor 216 and / or one or more main camera sensors 218 without departing from the scope of the present disclosure. In addition, although the AON camera sensor 216 and the main camera sensor 218 are shown as different sensors, one or more camera sensors that can operate in two or more different modes (e.g., AON mode, medium power mode, high power mode, etc.) may also be used without departing from the scope of the present disclosure.

[0072] Figure 5 is a flow chart illustrating an example of a process 500 for selecting a camera sensor from a plurality of available camera sensors. At block 502, the process 500 includes initializing a camera sensor selection operation. In some implementations, the process 500 may utilize a plurality of available camera sensors (e.g., Figure 2 In one illustrative example, one or more motion sensors (e.g., Figure 2Sensor 219 of the electronic device may detect movement of a device including multiple cameras into an image capture location. For example, process 500 may detect whether the device has moved a distance of approximately one foot, indicating that the device has been positioned in an image capture location (e.g., from a table and / or a user's pocket to a position in front of the user's face). In some cases, images from image sensors facing the user may be used to determine whether the user is looking at the scene. In another illustrative example, process 500 may initialize a camera sensor selection operation in response to a selection of a camera application by a user of the electronic device. Any other suitable technique for determining that a user may capture an image may also be used without departing from the scope of the present disclosure. In some cases, process 500 may be performed by obtaining a preliminary image from an active camera sensor while other available camera sensors are inactive (e.g., Figure 2 AON camera sensor 216) to initialize the camera selection process.

[0073] At block 504, process 500 may detect an image subject based on the preliminary image. For example, process 500 may be based on a machine learning engine (e.g., including Figure 4 In some cases, process 500 may detect subjects (e.g., one or more people, pets, objects, buildings, scenery, etc.) in the preliminary image. In some cases, process 500 may compare the detected subjects to previously captured subjects. For example, the detected subjects may be matched to subjects detected in previously captured images stored in a photo library of the electronic device.

[0074] At block 506, process 500 may determine a DOF range associated with the scene captured in the preliminary image. For example, process 500 may determine the closest subject and the farthest subject in the scene. In some cases, the DOF range associated with the scene may use the distance to the closest subject as the DOF range. 近 In some cases, the DOF range associated with a scene may use the distance to the farthest subject as D 远 In some implementations, process 500 can include expanding the DOF range (eg, by 5%, 10%, or any other amount) to account for errors in depth estimation and / or potential movement of the subject.

[0075] At block 508, process 500 may compare the DOF range associated with the scene captured in the preliminary image to each camera of a plurality of available cameras (eg, Figure 4 In some cases, the available DOF range of each of the multiple available cameras may be stored in a memory (e.g., Figure 2 SRAM 220, DRAM 214, system memory 915, cache 912 and / or Fig. 9 In some cases, process 500 may determine a numerical correspondence between a DOF range associated with a scene and each of the DOF ranges that may be provided by a plurality of available camera sensors. In an illustrative example, the numerical correspondence may be determined as a DOF score as shown in equation (4).

[0076] At block 510, process 500 may select a camera sensor from among the plurality of available camera sensors that provides a DOF range that best fits the DOF range associated with the scene (e.g., the camera and DOF range with the highest DOF score). In some cases, process 500 may also select a specific DOF range from among a plurality of DOF ranges available for the selected camera sensor (e.g., for a camera sensor with a variable aperture, lens position, etc.).

[0077] At block 512, process 500 may activate the selected camera and capture an image of the scene. For example, when process 500 obtains user input for capturing an image, the selected camera may be activated. In one illustrative example, when a user taps a portion of a display that includes a detected subject, process 500 may activate the selected camera. In some cases, when a user presses a capture and / or record button, the activated camera may be used to capture an image (or video) of the scene.

[0078] As described above, the image processing system 200, the camera selection subsystem 405, and related techniques herein may allow the system to select from a plurality of available cameras (e.g., Figure 2 The method of automatically and efficiently selecting a camera (or camera sensor) from the main camera sensor 218 of the AON for capturing a scene. For example, using a camera sensor (e.g., Figure 2 Based on a preliminary image captured by the AON camera sensor 216 of the plurality of cameras, these systems and techniques can determine a DOF range and / or FOV associated with a scene captured in the preliminary image. In some cases, the camera selection subsystem 405 can compare the DOF range that each of the plurality of cameras can provide with the determined DOF range associated with the scene. In some implementations, the camera selection subsystem 405 can compare the FOV associated with the FOV capability of each of the plurality of cameras.

[0079] By determining which of the available cameras to select based on a preliminary image, the systems and techniques described herein can avoid activating each of the multiple cameras to capture an image and determine which of the multiple cameras provides the best image quality. As the number of cameras available in an electronic device increases, the overall power savings provided by the systems and techniques can also increase. In an illustrative example, activating a camera to capture a preliminary image can consume 200 milliwatts (mW) of power. For an electronic device including five cameras, a total of 1 W of power can therefore be consumed. In some examples, using the systems and techniques described herein, an AON camera operating in a low power mode can determine which of the available cameras to select based on a preliminary image using a power between 20 mW and 30 mW.

[0080] Figure 6 is a flow chart illustrating an example of a process 600 of selecting a camera from a plurality of available cameras. At block 602, the process 600 includes obtaining an image from a camera (eg, AON camera sensor 216, primary camera sensor 218).

[0081] At block 604, process 600 includes determining a DOF range associated with a scene captured in an image (e.g., by Figure 4 In some aspects, the DOF range associated with the scene includes a near distance associated with the scene and a far distance associated with the scene.

[0082] At block 606, process 600 includes comparing a DOF range associated with the scene captured in the image to one or more DOF ranges of corresponding cameras in the plurality of cameras (e.g., via DOF comparison subsystem 440). In some examples, determining the DOF range associated with the scene captured in the image includes determining a near distance associated with the scene based on the image and determining a far distance associated with the scene based on the image. In some cases, the DOF range associated with the scene is determined based at least in part on detecting one or more identified people, friends, pets, or objects from the image.

[0083] At block 608, process 600 includes determining a selected camera from a plurality of cameras based on comparing a DOF range associated with a scene captured in the image to one or more DOF ranges corresponding to each of the plurality of cameras. In some aspects, process 600 includes capturing additional images by the selected camera. In some implementations, the selected camera includes the camera from which the image was obtained. In some cases, the selected camera includes a second camera different from the camera. In some implementations, the plurality of cameras does not include the camera from which the image was obtained.

[0084] In some examples, process 600 includes detecting a first subject in a scene captured in an image. In some cases, determining a DOF range associated with the scene captured in the image includes determining at least one of a near distance associated with the first subject and a far distance associated with the first subject. In some cases, process 600 includes detecting a second subject in the scene captured in the image. In some examples, determining a DOF range associated with the scene captured in the image includes determining a near distance associated with the first subject and a far distance associated with the second subject.

[0085] In some cases, a first camera in the plurality of cameras includes a variable aperture having at least a first aperture size and a second aperture size. In some examples, a first DOF range of the first camera is associated with the first aperture size, and a second DOF range different from the first DOF range is associated with the second aperture size. For example, in some cases, a first camera in the plurality of cameras is associated with a first DOF range and a second DOF range different from the first DOF range. In some examples, the first DOF range is associated with a first aperture size, and the second DOF range is associated with a second aperture size different from the first aperture size.

[0086] In some implementations, a first DOF range associated with a first camera in the plurality of cameras includes a first close distance and a first long distance. In some examples, a second DOF range associated with a second camera in the plurality of cameras includes a second close distance and a second long distance. In some cases, the DOF range associated with the scene at least partially overlaps both the first DOF range and the second DOF range.

[0087] In some examples, the processes described herein (e.g., process 500, process 600, and / or other processes described herein) can be performed by a computing device or apparatus. In one example, one or more of these processes can be performed by Figure 2 In another example, one or more of these processes may be performed by Fig. 9 The computing system 900 shown executes. For example, Fig. 9 The computing device of the computing system 900 shown may include components of the camera selection system or any combination thereof and may implement Figure 5 The process of 500 Figure 6 Operations of process 600 and / or other processes described herein.

[0088] The computing device may include any suitable device, such as a vehicle or a computing device of a vehicle (e.g., a driver monitoring system (DMS) of a vehicle), a mobile device (e.g., a mobile phone), a desktop computing device, a tablet computing device, a wearable device (e.g., a VR headset, an AR headset, AR glasses, a network-connected watch or smart watch or other wearable device), a server computer, a robotic device, a television, and / or any other computing device with resource capabilities to perform the processes described herein (including processes 500, 600, and / or any other processes described herein). In some cases, the computing device or apparatus may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more cameras, one or more sensors, and / or other components configured to perform the steps of the processes described herein. In some examples, the computing device may include a display, a network interface configured to communicate and / or receive data, any combination thereof, and / or other components. The network interface may be configured to communicate and / or receive data based on an Internet Protocol (IP) or other types of data.

[0089] The components of the computing device may be implemented in circuits. For example, the components may include and / or may be implemented using electronic circuits or other electronic hardware, which may include one or more programmable electronic circuits (e.g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), and / or other suitable electronic circuits), and / or may include and / or may be implemented using computer software, firmware, or any combination thereof for performing the various operations described herein.

[0090] Process 500 and process 600 are illustrated as a logic flow diagram, the operations of which represent a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, each operation represents a computer-executable instruction stored on one or more computer-readable storage media that, when executed by one or more processors, performs the described operation. Generally speaking, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform specific functions or implement specific data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations may be combined in any order and / or in parallel to implement the process.

[0091] In addition, processes 500, 600, and / or other processes described herein may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed together on one or more processors, through hardware, or a combination thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program that includes multiple instructions that can be executed by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.

[0092] As described above, various aspects of the present disclosure may utilize a machine learning model or system. Figure 7 is an illustrative example of a deep learning neural network 700 that can be used to implement feature extraction based on machine learning. In an illustrative example, feature extraction can be used to detect a subject in a scene captured in a preliminary image. Input layer 720 includes input data. In an illustrative example, input layer 720 may include data representing pixels of an input image. Neural network 700 includes multiple hidden layers 722a, 722b to 722n. Hidden layers 722a, 722b to 722n include "n" hidden layers, where "n" is an integer greater than or equal to one. The multiple hidden layers can include as many layers as required for a given application. Neural network 700 also includes an output layer 721, which provides an output generated by the processing performed by hidden layers 722a, 722b to 722n. In an illustrative example, output layer 721 can provide a classification of objects in the input image. The classification may include a category that identifies the type of subject (e.g., a person, a friend, a pet, an object, a building, a landscape, etc.).

[0093] Neural network 700 is a multi-layer neural network composed of interconnected nodes. Each node can represent a piece of information. The information associated with these nodes is shared between different layers, and each layer retains the information as it processes it. In some cases, neural network 700 may include a feedforward network in which there is no feedback connection in which the output of the network is fed back to itself. In some cases, neural network 700 may include a recurrent neural network, which may have loops that allow information to be carried across nodes when reading in input.

[0094] Information can be exchanged between nodes by nodes to node interconnects between each layer. The nodes of input layer 720 can activate a group of nodes in the first hidden layer 722a. For example, as shown in the figure, each input node in the input nodes of input layer 720 is connected to each node in the nodes of the first hidden layer 722a. The nodes of the first hidden layer 722a can transform the information of each input node by applying an activation function to the input node information. The information derived from the transformation can then be passed to the nodes of the next hidden layer 722b and can activate the nodes of the next hidden layer, and the nodes of the next hidden layer can perform their own specified functions. Example functions include convolution, upsampling, data conversion and / or any other suitable function. Then, the output of hidden layer 722b can activate the nodes of the next hidden layer, and so on. The output of the last hidden layer 722n can activate one or more nodes of output layer 721, and output is provided at the one or more nodes. In some cases, although a node in neural network 700 (e.g., node 726) is shown as having multiple output lines, the node has a single output, and all lines shown as output from the node represent the same output value.

[0095] In some cases, each node or interconnection between nodes may have a weight, which is a set of parameters derived from the training of neural network 700. Once neural network 700 is trained, it may be referred to as a trained neural network that can be used to classify one or more activities. For example, an interconnection between nodes may represent a piece of information learned about the interconnected nodes. The interconnection may have a tunable digital weight that can be tuned (e.g., based on a training data set), allowing neural network 700 to adapt to the input and be able to learn as more and more data is processed.

[0096] The neural network 700 is pre-trained to process features from the data in the input layer 720 using different hidden layers 722a, 722b to 722n to provide an output through the output layer 721. In an example where the neural network 700 is used to identify subjects in a preliminary image, the neural network 700 may be trained using training data including both images and labels, as described above. For example, training images may be input to the network, where each training image has a label indicating a feature in the image. In one example using object classification for illustrative purposes, the training images may include an image of the number 2, in which case the label of the image may be [0 0 1 0 0 0 0 0 0 0].

[0097] In some cases, the neural network 700 may use a training process known as back propagation to adjust the weights of the nodes. As described above, the back propagation process may include a forward pass, a loss function, a backward pass, and a weight update. The forward pass, loss function, backward pass, and parameter update are performed for one training iteration. For each set of training images, this process may be repeated a certain number of iterations until the neural network 700 is trained well enough so that the weights of each layer are accurately tuned.

[0098] For the example of identifying an object (or subject) in an image, a forward pass may include passing a training image through the neural network 700. Prior to training the neural network 700, the weights are initially randomized. As an illustrative example, an image may include an array of numbers representing pixels of the image. Each number in the array may include a value from 0 to 255 describing the intensity of the pixel at that position in the array. In one example, the array may include a 28×28×3 digital array having 28 rows and 28 columns of pixels and 3 color components (e.g., red, green, and blue, or a brightness and two chrominance components, etc.).

[0099] As described above, for the first training iteration of the neural network 700, the output will likely include values ​​that do not prioritize any particular class due to the weights randomly selected at initialization. For example, if the output is a vector with probabilities that the object includes different classes, the probability values ​​for each of the different classes may be equal or at least very similar (e.g., for ten possible classes, each class may have a probability value of 0.1). Using the initial weights, the neural network 700 cannot determine low-level features and therefore cannot accurately determine what the classification of the object may be. A loss function may be used to analyze the errors in the output. Any suitable loss function definition may be used, such as a cross entropy loss. Another example of a loss function includes the mean squared error (MSE), which is defined as The loss can be set equal to E 总 The value of .

[0100] For the first training images, the loss (or error) will be high because the actual values ​​will be very different from the predicted outputs. The goal of training is to minimize the amount of loss so that the predicted outputs are the same as the training labels. The neural network 700 can perform a backward pass by determining which inputs (weights) contribute the most to the loss of the network, and the weights can be adjusted so that the loss decreases and is ultimately minimized. The derivative of the loss with respect to the weights (expressed as dL / dW, where W is the weight at a particular layer) can be calculated to determine the weights that contribute the most to the loss of the network. After calculating the derivatives, a weight update can be performed by updating all the weights of the filter. For example, the weights can be updated so that they change in the opposite direction of the gradient. The weight update can be expressed as Where w represents the weight, w i Represents the initial weight, and η represents the learning rate. The learning rate can be set to any suitable value, where a high learning rate includes a larger weight update, while a lower value indicates a smaller weight update.

[0101] The neural network 700 may include any suitable deep network. An example includes a convolutional neural network (CNN), which includes an input layer and an output layer with multiple hidden layers between the input layer and the output layer. The hidden layers of the CNN include a series of convolutional layers, nonlinear layers, pooling layers (for downsampling), and fully connected layers. The neural network 700 may include any other deep network other than a CNN, such as an autoencoder, a deep belief network (DBN), a recurrent neural network (RNN), etc.

[0102] Figure 8 is an illustrative example of a convolutional neural network (CNN) 800. The input layer 820 of the CNN 800 includes data representing an image or frame. For example, the data may include an array of numbers representing pixels of the image, where each number in the array includes a value from 0 to 255 describing the intensity of the pixel at the position of the number in the array. Using the previous example from above, the array may include a 28×28×3 digital array with 28 rows and 28 columns of pixels and 3 color components (e.g., red, green, and blue, or brightness and two chrominance components, etc.). The image may be passed through a convolutional hidden layer 822a, an optional non-linear activation layer, a pooling hidden layer 822b, and a fully connected hidden layer 822c to obtain an output at an output layer 824. Although Figure 8 Only one hidden layer is shown in the various hidden layers, but a person of ordinary skill will understand that multiple convolutional hidden layers, non-linear layers, pooling hidden layers, and / or fully connected layers may be included in CNN 800. As previously described, the output may indicate a single category of an object, or may include a probability of a category that best describes an object in the image.

[0103] The first layer of CNN 800 is a convolutional hidden layer 822a. The convolutional hidden layer 822a analyzes the image data of the input layer 820. Each node of the convolutional hidden layer 822a is connected to a node (pixel) area in the input image called a receptive field. The convolutional hidden layer 822a can be considered as one or more filters (each filter corresponds to a different activation or feature map), where each convolution iteration of the filter is a node or neuron of the convolutional hidden layer 822a. For example, the area of ​​the input image covered by the filter at each convolution iteration will be the receptive field of the filter. In an illustrative example, if the input image includes a 28×28 array and each filter (and corresponding receptive field) is a 5×5 array, there will be 24×24 nodes in the convolutional hidden layer 822a. Each connection between a node and the receptive field of the node learns a weight, and in some cases learns an overall bias, so that each node learns to analyze its specific local receptive field in the input image. Each node of the hidden layer 822a will have the same weights and biases (referred to as shared weights and shared biases). For example, the filter has an array of weights (numbers) and the same depth as the input. For the image example, the filter will have a depth of 3 (according to the three color components of the input image). An illustrative example size of the filter array is 5×5×3, corresponding to the size of the receptive field of the node.

[0104] The convolutional nature of the convolutional hidden layer 822a is due to the fact that each node of the convolutional layer is applied to its corresponding receptive field. For example, the filter of the convolutional hidden layer 822a may start at the upper left corner of the input image array and may be convolved around the input image. As described above, each convolution iteration of the filter may be considered as a node or neuron of the convolutional hidden layer 822a. In each convolution iteration, the value of the filter is multiplied by the original pixel values ​​of the corresponding number of the image (e.g., a 5×5 filter array is multiplied by a 5×5 array of input pixel values ​​at the upper left corner of the input image array). The multiplications from each convolution iteration may be added together to obtain the sum of the iteration or node. Next, the process is continued at the next position in the input image according to the receptive field of the next node in the convolutional hidden layer 822a. For example, the filter may be moved by a step amount (referred to as a stride) to the next receptive field. The stride may be set to 1 or other suitable amount. For example, if the stride is set to 1, the filter will move 1 pixel to the right at each convolution iteration. Processing the filter at each unique location in the input volume produces a number representing the filter result at that location, thereby determining a summed value for each node of the convolutional hidden layer 822a.

[0105] The mapping from the input layer to the convolutional hidden layer 822a is called an activation map (or feature map). The activation map includes the value of each node, which represents the filter result at each location of the input volume. The activation map may include an array that includes the various summed values ​​produced by each iteration of the filter over the input volume. For example, if a 5×5 filter is applied to each pixel of a 28×28 input image (with a stride of 1), the activation map will include a 24×24 array. The convolutional hidden layer 822a may include several activation maps in order to identify multiple features in the image. Figure 8 The example shown includes three activation maps. Using the three activation maps, the convolutional hidden layer 822a can detect three different kinds of features, each of which is detectable across the entire image.

[0106] In some examples, a nonlinear hidden layer may be applied after the convolutional hidden layer 822a. A nonlinear layer may be used to introduce nonlinearity into a system that already computes linear operations. An illustrative example of a nonlinear layer is a rectified linear unit (ReLU) layer. The ReLU layer may apply the function f(x)=max(0,x) to all values ​​in the input volume, which changes all negative activations to 0. Thus, the ReLU may increase the nonlinear properties of the CNN 800 without affecting the receptive field of the convolutional hidden layer 822a.

[0107] The pooling hidden layer 822b may be applied after the convolutional hidden layer 822a (and after the nonlinear hidden layer when used). The pooling hidden layer 822b is used to simplify the information in the output from the convolutional hidden layer 822a. For example, the pooling hidden layer 822b may take each activation map output from the convolutional hidden layer 822a and generate a condensed activation map (or feature map) using a pooling function. Max pooling is an example of a function performed by a pooling hidden layer. The pooling hidden layer 822a uses other forms of pooling functions, such as average pooling, L2 norm pooling, or other suitable pooling functions. A pooling function (e.g., a maximum pooling filter, an L2 norm filter, or other suitable pooling filter) is applied to each activation map included in the convolutional hidden layer 822a. In Figure 8 In the example shown, three pooling filters are used for the three activation maps in the convolutional hidden layer 822a.

[0108] In some examples, max pooling may be used by applying a max pooling filter (e.g., having a size of 2×2) with a stride (e.g., equal to the dimension of the filter, such as a stride of 2) to the activation map output from the convolutional hidden layer 822a. The output from the max pooling filter includes the maximum number in each sub-region of the filter convolution. Using a 2×2 filter as an example, each unit in the pooling layer may summarize a region of 2×2 nodes in the previous layer (where each node is a value in the activation map). For example, four values ​​(nodes) in the activation map will be analyzed by a 2×2 max pooling filter at each iteration of the filter, where the maximum of the four values ​​is output as the “maximum” value. If such a max pooling filter is applied to an activation filter having a dimension of 24×24 nodes from the convolutional hidden layer 822a, the output from the pooling hidden layer 822b will be an array of 12×12 nodes.

[0109] In some examples, an L2 norm pooling filter may also be used. The L2 norm pooling filter involves calculating the square root of the sum of the squares of the values ​​in a 2×2 region (or other suitable region) of the activation map (rather than calculating the maximum value as done in max pooling), and using the calculated value as output.

[0110] Intuitively, a pooling function (e.g., max pooling, L2 norm pooling, or other pooling functions) determines whether a given feature is found anywhere in a region of an image, and discards the exact location information. This can be done without affecting the results of feature detection, because once a feature has been found, the exact location of the feature is not as important as its approximate location relative to other features. Max pooling (and other pooling methods) provides the following benefits: far fewer features are pooled, thereby reducing the number of parameters required in subsequent layers of CNN 800.

[0111] The last layer of connections in the network is a fully connected layer that connects each node from the pooling hidden layer 822b to each of the output nodes in the output layer 824. Using the above example, the input layer includes 28×28 nodes that encode the pixel intensities of the input image, the convolutional hidden layer 822a includes 3×24×24 hidden feature nodes based on applying a 5×5 local receptive field (for the filter) to the three activation maps, and the pooling hidden layer 822b includes a layer of 3×12×12 hidden feature nodes based on applying a maximum pooling filter to a 2×2 region on each of the three feature maps. Extending this example, the output layer 824 may include ten output nodes. In such an example, each node of the 3×12×12 pooling hidden layer 822b is connected to each node of the output layer 824.

[0112] The fully connected layer 822c can obtain the output of the previous pooled hidden layer 822b (which should represent an activation map of high-level features) and determine the features most relevant to a particular category. For example, the fully connected layer 822c layer can determine the high-level features that are most strongly associated with a particular category and can include weights (nodes) for the high-level features. The product between the weights of the fully connected layer 822c and the pooled hidden layer 822b can be calculated to obtain probabilities for different categories. For example, if CNN 800 is used to predict that the object in the image is a person, there will be high values ​​in the activation map representing the high-level features of the person (e.g., there are two legs, there is a face at the top of the object, there are two eyes at the upper left and upper right of the face, there is a nose in the middle of the face, there is a mouth at the bottom of the face, and / or other features common to people).

[0113] In some examples, the output from the output layer 824 may include an M-dimensional vector (in the previous example, M=10). M indicates the number of categories that the CNN 800 must choose from when classifying objects in the image. Other example outputs may also be provided. Each number in the M-dimensional vector may represent the probability that the object belongs to a certain category. In an illustrative example, if the 10-dimensional output vector represents that objects of ten different categories are [0 0 0.05 0.8 0 0.15 0 0 0 0], the vector indicates that the image is a 5% probability of an object of the third category (e.g., a dog), the image is an 80% probability of an object of the fourth category (e.g., a person), and the image is a 15% probability of an object of the sixth category (e.g., a kangaroo). The probability for a category can be considered as the confidence level that the object is part of that category.

[0114] Fig. 9 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. Specifically, Fig. 9 An example of a computing system 900 is illustrated, which may be any computing device, for example, constituting an internal computing system, a remote computing system, a camera, or any components thereof, wherein the components of the system communicate with each other using a connection 905. The connection 905 may be a physical connection using a bus, or a direct connection into the processor 910, such as in a chipset architecture. The connection 905 may also be a virtual connection, a networked connection, or a logical connection.

[0115] In some embodiments, computing system 900 is a distributed system, where the functionality described in the present disclosure may be distributed within a data center, multiple data centers, a peer-to-peer network, etc. In some embodiments, one or more of the described system components represent a number of such components that each perform some or all of the functionality for which the component is described. In some embodiments, a component may be a physical device or a virtual device.

[0116] The example computing system 900 includes at least one processing unit (CPU or processor) 910 and connections 905 that couple various system components including system memory 915 such as read-only memory (ROM) 920 and random access memory (RAM) 925 to the processor 910. The computing system 900 may include a cache 912 of high-speed memory directly connected to the processor 910, in close proximity to the processor, or integrated as part of the processor.

[0117] Processor 910 may include any general purpose processor and hardware or software services, such as services 932, 934, and 936 stored in storage device 930, that are configured to control processor 910 as well as a dedicated processor where software instructions are incorporated into the actual processor design. Processor 910 may essentially be a completely independent computing system containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0118] To enable user interaction, the computing system 900 includes an input device 945 that can represent any number of input mechanisms, such as a microphone for voice, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, voice, and the like. The computing system 900 may also include an output device 935 that can be one or more of a plurality of output mechanisms. In some cases, a multimodal system may enable a user to provide multiple types of input / output to communicate with the computing system 900. The computing system 900 may include a communication interface 940, which may generally govern and manage user input and system output. The communication interface may perform or facilitate receiving and / or sending wired or wireless communications using a wired and / or wireless transceiver, including utilizing an audio jack / plug, a microphone jack / plug, a universal serial bus (USB) port / plug, Ports / plugs, Ethernet ports / plugs, Fiber optic ports / plugs, Dedicated wired ports / plugs, Wireless signal transfer, Low Energy (BLE) wireless signal transfer, Wireless signal transfer, radio frequency identification (RFID) wireless signal transfer, near field communication (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, visible light communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), infrared (IR) communication wireless signal transfer, public switched telephone network (PSTN) signal transfer, integrated services digital network (ISDN) signal transfer, 3G / 4G / 5G / LTE cellular data network wireless signal transfer, ad hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communication interface 940 may also include one or more global navigation satellite system (GNSS) receivers or transceivers for determining the location of the computing system 900 based on receiving one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the United States' Global Positioning System (GPS), Russia's Global Navigation Satellite System (GLONASS), China's BeiDou Navigation Satellite System (BDS), and Europe's Galileo GNSS. There is no restriction to operating on any particular hardware arrangement, and thus the base features herein may be easily substituted for improved hardware or firmware arrangements as they are developed.

[0119] The storage device 930 may be a non-volatile and / or non-transitory and / or computer-readable memory device and may be a hard disk or other type of computer-readable medium that can store data that can be accessed by a computer, such as a magnetic tape cartridge, a flash memory card, a solid-state memory device, a digital versatile disk, a cassette, a floppy disk, a flexible disk, a hard disk, a magnetic tape, a magnetic stripe / strip, any other magnetic storage medium, a flash memory, a memristor memory, any other solid-state memory, a compact disk-read only memory (CD-ROM) optical disk, a rewritable compact disk (CD) optical disk, a digital video disk (DVD) optical disk, a Blu-ray disc (BDD) optical disk, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a memory ... card, a smart card chip, an EMV chip, a subscriber identity module (SIM) card, a mini / micro / nano / pico SIM card, another integrated circuit (IC) chip / card, a random access memory (RAM), a static RAM (SRAM), a dynamic RAM (DRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash EPROM (FLASHEPROM), a cache memory (L1 / L2 / L3 / L4 / L5 / L#), a resistive random access memory (RRAM / ReRAM), a phase change memory (PCM), a spin-transfer torque RAM (STT-RAM), another memory chip or box, and / or a combination thereof.

[0120] Storage device 930 may include software services, servers, services, etc., which, when the code defining such software is executed by processor 910, causes the system to perform functions. In some embodiments, hardware services that perform specific functions may include software components for performing functions stored in a computer-readable medium connected to necessary hardware components such as processor 910, connector 905, output device 935, etc.

[0121] As used herein, the term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing or carrying instructions and / or data. Computer-readable media may include non-transient media in which data may be stored and does not include carrier waves and / or transient electronic signals that are propagated wirelessly or on a wired connection. Examples of non-transient media may include, but are not limited to, disks or tapes, optical storage media (such as compact disks (CDs) or digital versatile disks (DVDs)), flash memory, memory, or memory devices. Computer-readable media may store thereon code and / or machine-executable instructions, which may represent procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, categories, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by transmitting and / or receiving information, data, independent variables, parameters, or memory contents. Information, independent variables, parameters, data, etc. may be transmitted, forwarded, or sent using any suitable means, including memory sharing, message passing, token passing, network sending, etc.

[0122] In some embodiments, computer-readable storage devices, media, and memories may include wired or wireless signals including bit streams, etc. However, when referred to, non-transitory computer-readable storage media specifically excludes media such as power consumption, carrier signals, electromagnetic waves, and signals themselves.

[0123] Specific details are provided in the above description to provide a thorough understanding of the embodiments and examples provided herein. However, it will be appreciated by those skilled in the art that embodiments can be put into practice without these specific details. For clarity, in some cases, the present technology may be presented as including separate functional blocks, including functional blocks comprising devices, device components, steps or routines in the method embodied in a combination of software or hardware and software. Additional components other than those components shown in the accompanying drawings and / or described herein may be used. For example, circuits, systems, networks, processes and other components may be shown as components in block diagram form to avoid obscuring these embodiments in unnecessary details. In other cases, known circuits, processes, algorithms, structures and techniques may be shown without necessary details to avoid confusing each embodiment.

[0124] Individual embodiments may be described above as processes or methods depicted as flow charts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. Although a flow chart may describe an operation as a sequential process, many operations in the operation may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. The process is terminated when the operation of the process is completed, but the process may have additional steps not included in the accompanying drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, the termination of the process may correspond to the function returning to a calling function or a main function.

[0125] The processes and methods according to the above examples can be implemented using stored computer executable instructions or otherwise obtained from computer readable media. These instructions may include, for example, instructions and data that configure a general-purpose computer, a special-purpose computer, or a processing device to perform a certain function or function group. Parts of the computer resources used can be accessed through a network. Computer executable instructions can be, for example, binary, intermediate format instructions, such as assembly language, firmware, source code, etc. Examples of computer readable media that can be used to store instructions, information used, and / or information created during the methods according to the described examples include disks or optical disks, flash memory, USB devices with non-volatile memory, networked storage devices, etc.

[0126] The equipment implementing the processes and methods according to these disclosures may include hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof, and may take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., computer program products) for performing necessary tasks may be stored in a computer-readable or machine-readable medium. The processor may perform the necessary tasks. Typical examples of form factors include laptop computers, smart phones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mounted devices, stand-alone devices, etc. The functionality described herein may also be embodied in peripheral devices or plug-in cards. By way of additional examples, such functionality may also be implemented on circuit boards among different chips or different processes executed on a single device.

[0127] Instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0128] In the foregoing description, various aspects of the present application are described with reference to the specific embodiments of the present application, but those skilled in the art will recognize that the present application is not limited thereto. Thus, although the exemplary embodiments of the present application have been described in detail herein, it is to be understood that the inventive concept can be embodied and adopted in various other ways, and the appended claims are intended to be interpreted as including these variations, unless limited by the prior art. Various features and aspects of the above-mentioned applications can be used individually or in combination. In addition, without departing from the broader essence and scope of this specification, the embodiments can be used in any number of environments and applications beyond the environments and applications described herein. Therefore, the description and the accompanying drawings should be considered as illustrative rather than restrictive. For the purpose of illustration, each method is described in a specific order. It should be understood that in an alternative embodiment, each method can be performed in a different order than described.

[0129] One of ordinary skill will understand that the less than ("<") and greater than (">") symbols or terms used herein may be replaced with less than or equal to ("≤") and greater than or equal to ("≥") symbols, respectively, without departing from the scope of the present specification.

[0130] Where a component is described as being “configured to” perform certain operations, such configuration may be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., a microprocessor or other suitable electronic circuits) to perform the operations, or any combination thereof.

[0131] The phrase "coupled to" means that any component is directly or indirectly physically connected to another component, and / or any component is directly or indirectly in communication with another component (e.g., connected to another component via a wired or wireless connection and / or other suitable communication interface).

[0132] Claim language or other language stating "at least one of" a set and / or "one or more of" a set indicates that one member of the set or multiple members of the set (in any combination) satisfies the claim. For example, claim language stating "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language stating "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language "at least one of" a set and / or "one or more of" a set does not limit the set to the items listed in the set. For example, claim language stating "at least one of A and B" or "at least one of A or B" may mean A, B, or A and B, and may additionally include items not listed in the set of A and B.

[0133] Various exemplary logic blocks, modules, circuits and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, firmware or a combination thereof. In order to clearly illustrate this interchangeability of hardware and software, various exemplary components, frames, modules, circuits and steps have been generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on specific applications and the design constraints proposed to the entire system. Technicians can implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be interpreted as making it depart from the scope of the application.

[0134] The technology described herein can also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such technology can be implemented in any of a variety of devices, such as general-purpose computers, wireless communication device mobile phones, or integrated circuit devices with multiple uses, including applications in wireless communication device mobile phones and other devices. Any features described as modules or components can be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, these technologies can be implemented at least in part by a computer-readable data storage medium including a program code, the program code including instructions, which execute one or more of the above methods when executed. Computer-readable data storage media can form a part of a computer program product, which can include packaging materials. Computer-readable media can include memory or data storage media, such as random access memory (RAM) (such as synchronous dynamic random access memory (SDRAM)), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, etc. Additionally or alternatively, the technology may be implemented at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as a propagated signal or wave.

[0135] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such processors may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Therefore, the term "processor" as used herein may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or device suitable for implementing the techniques described herein.

[0136] Exemplary aspects of the present disclosure include:

[0137] Aspect 1. A method for selecting a camera from a plurality of cameras, the method comprising: obtaining an image from a camera; determining a depth of field (DOF) range associated with a scene captured in the image; comparing the DOF range associated with the scene captured in the image with one or more DOF ranges of corresponding cameras in a plurality of cameras; and determining a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the first image with the corresponding one or more DOF ranges of each of the plurality of cameras.

[0138] Aspect 2. The method according to aspect 1, further comprising capturing additional images by the selected camera.

[0139] Aspect 3. A method according to any one of Aspects 1 to 2, the method also includes determining a user's intention to capture an image based on the first image captured by the camera, wherein the camera is active and a selected camera among the multiple cameras is inactive.

[0140] Aspect 4. A method according to any one of Aspects 1 to 3, the method further comprising: displaying the additional image on a display; after displaying the additional image on the display, obtaining an additional DOF range different from the DOF range associated with the scene; obtaining a third image from the multiple cameras based on comparing the additional DOF range with the one or more DOF ranges of corresponding cameras in the multiple cameras; and capturing a third image by the additional selected camera.

[0141] Aspect 5. The method according to any one of aspects 1 to 4, wherein the additional DOF range is obtained based on user input.

[0142] Aspect 6. The method according to any one of aspects 1 to 5, wherein the DOF range associated with the scene includes a near distance associated with the scene and a far distance associated with the scene.

[0143] Aspect 7. A method according to any one of Aspects 1 to 6, the method also includes detecting a first subject in the scene captured in the image, wherein determining the DOF range associated with the scene captured in the image includes determining at least one of a close distance associated with the first subject and a long distance associated with the first subject.

[0144] Aspect 8. A method according to any one of Aspects 1 to 7, the method also includes detecting a second subject in the scene captured in the first image, wherein determining the DOF range associated with the scene captured in the first image includes determining the near distance associated with the first subject and the far distance associated with the second subject.

[0145] Aspect 9. A method according to any one of Aspects 1 to 8, wherein comparing the DOF range associated with the scene with the one or more DOF ranges of corresponding cameras in the multiple cameras includes determining a numerical correspondence between the near distance of acceptable focus, the far distance of acceptable focus, and the corresponding one or more DOF ranges of each camera in the multiple cameras.

[0146] Aspect 10. A method according to any one of Aspects 1 to 9, wherein determining the DOF range associated with the scene captured in the image comprises: determining a near distance associated with the scene based on the image; and determining a far distance associated with the scene based on the image.

[0147] Aspect 11. A method according to any one of aspects 1 to 10, wherein the DOF range associated with the scene is determined at least in part based on detecting one or more identified people, friends, pets, or objects from the image.

[0148] Aspect 12. A method according to any one of Aspects 1 to 11, wherein a first camera of the plurality of cameras comprises a variable aperture having at least a first aperture size and a second aperture size, wherein a first DOF range of the first camera is associated with the first aperture size, and a second DOF range different from the first DOF range is associated with the second aperture size.

[0149] Aspect 13. The method according to any one of aspects 1 to 12, wherein a first camera of the plurality of cameras is associated with a first DOF range and a second DOF range different from the first DOF range.

[0150] Aspect 14. The method according to any one of aspects 1 to 13, wherein the first DOF range is associated with a first aperture size, and the second DOF range is associated with a second aperture size different from the first aperture size.

[0151] Clause 15. The method according to any one of clauses 1 to 14, wherein the selected camera comprises the camera from which the first image was obtained.

[0152] Clause 16. The method according to any one of clauses 1 to 15, wherein the selected camera comprises a second camera different from the camera.

[0153] Aspect 17. The method according to any one of aspects 1 to 16, wherein the plurality of cameras does not include the camera from which the first image was obtained.

[0154] Aspect 18. A method according to any one of Aspects 1 to 17, wherein a first DOF range associated with a first camera among the multiple cameras includes a first close distance and a first long distance; a second DOF range associated with a second camera among the multiple cameras includes a second close distance and a second long distance; and the DOF range associated with the scene at least partially overlaps with both the first DOF range and the second DOF range.

[0155] Aspect 19. The method according to any one of aspects 1 to 18, wherein in response to determining that a user intends to capture an image, the DOF range associated with the scene captured in the image is determined.

[0156] Aspect 20. A method according to any one of Aspects 1 to 19, wherein comparing the DOF range associated with the scene with the corresponding one or more DOF ranges of each of the multiple cameras includes determining a numerical correspondence between the DOF range associated with the scene and the corresponding one or more DOF ranges of each of the multiple cameras.

[0157] Aspect 21. The method according to any one of aspects 1 to 20, wherein determining the numerical correspondence comprises one or more of a percentage value, a ratio, a proportion, a function or a relationship.

[0158] Aspect 22. A method according to any one of Aspects 1 to 21, wherein comparing the DOF range associated with the scene with the corresponding one or more DOF ranges of each of the multiple cameras includes: determining a first numerical correspondence between the DOF range associated with the scene and a first DOF range associated with a first camera among the multiple cameras; and determining a second numerical correspondence between the DOF range associated with the scene and a second DOF range associated with the first camera among the multiple cameras, wherein the second DOF range is different from the first DOF range.

[0159] Aspect 23: An apparatus for selecting a camera from a plurality of cameras. The apparatus comprises a memory (e.g., implemented in a circuit) and a processor (or processors) coupled to the memory. The processor (or processors) is configured to: obtain an image from a camera; determine a depth of field (DOF) range associated with a scene captured in the image; compare the DOF range associated with the scene captured in the image with one or more DOF ranges of corresponding cameras in a plurality of cameras; and determine a selected camera from the plurality of cameras based on comparing the DOF range associated with the scene captured in the first image with the corresponding one or more DOF ranges of each of the plurality of cameras.

[0160] Aspect 24: The apparatus of aspect 23, wherein the processor is configured to: capture an additional image by the selected camera.

[0161] Aspect 25: An apparatus according to any one of Aspects 23 to 24, wherein the processor is configured to: determine the user's intention to capture an image based on the first image captured by the camera, wherein the camera is active and the selected camera among the multiple cameras is inactive.

[0162] Aspect 26: An apparatus according to any one of Aspects 23 to 25, wherein the processor is configured to: display the additional image on a display; after displaying the additional image on the display, obtain an additional DOF range that is different from the DOF range associated with the scene; from the multiple cameras based on comparing the additional DOF range with the one or more DOF ranges of corresponding cameras in the multiple cameras; and capture a third image by the additional selected camera.

[0163] Aspect 27: An apparatus according to any one of aspects 23 to 26, wherein the additional DOF range is obtained based on user input.

[0164] Aspect 28: The apparatus according to any one of Aspects 23 to 27, wherein the DOF range associated with the scene includes a near distance associated with the scene and a far distance associated with the scene.

[0165] Aspect 29: An apparatus according to any one of Aspects 23 to 28, wherein the processor is configured to: detect a first subject in the scene captured in the image, wherein determining the DOF range associated with the scene captured in the image includes determining at least one of a close distance associated with the first subject and a long distance associated with the first subject.

[0166] Aspect 30: An apparatus according to any one of Aspects 23 to 29, wherein the processor is configured to: detect a second subject in the scene captured in the first image, wherein determining the DOF range associated with the scene captured in the first image includes determining the near distance associated with the first subject and the long distance associated with the second subject.

[0167] Aspect 31: An apparatus according to any one of Aspects 23 to 30, wherein comparing the DOF range associated with the scene with the one or more DOF ranges of corresponding cameras in the multiple cameras includes determining a numerical correspondence between the near distance of acceptable focus, the far distance of acceptable focus, and the corresponding one or more DOF ranges of each camera in the multiple cameras.

[0168] Aspect 32: An apparatus according to any one of Aspects 23 to 31, wherein the processor is configured to: determine a near distance associated with the scene based on the image; and determine a far distance associated with the scene based on the image.

[0169] Aspect 33: An apparatus according to any one of Aspects 23 to 32, wherein the DOF range associated with the scene is determined at least in part based on detecting one or more identified people, friends, pets, or objects from the image.

[0170] Aspect 34: An apparatus according to any one of Aspects 23 to 33, wherein a first camera of the plurality of cameras comprises a variable aperture having at least a first aperture size and a second aperture size, wherein a first DOF range of the first camera is associated with the first aperture size, and a second DOF range different from the first DOF range is associated with the second aperture size.

[0171] Clause 35: The apparatus of any one of Clauses 23 to 34, wherein a first camera of the plurality of cameras is associated with a first DOF range and a second DOF range different from the first DOF range.

[0172] Aspect 36: The apparatus of any one of aspects 23 to 35, wherein the first DOF range is associated with a first aperture size, and the second DOF range is associated with a second aperture size different from the first aperture size.

[0173] Clause 37: The apparatus of any one of Clauses 23 to 36, wherein the selected camera comprises the camera from which the first image was obtained.

[0174] Clause 38: An apparatus according to any one of Clauses 23 to 37, wherein the selected camera comprises a second camera different from the camera.

[0175] Clause 39: The apparatus of any one of Clauses 23 to 38, wherein the plurality of cameras does not include the camera from which the first image was obtained.

[0176] Aspect 40: An apparatus according to any one of Aspects 23 to 39, wherein a first DOF range associated with a first camera among the multiple cameras includes a first close distance and a first long distance; a second DOF range associated with a second camera among the multiple cameras includes a second close distance and a second long distance; and the DOF range associated with the scene at least partially overlaps with both the first DOF range and the second DOF range.

[0177] Clause 41: An apparatus according to any one of Clauses 23 to 40, wherein in response to determining that a user intends to capture an image, the DOF range associated with the scene captured in the image is determined.

[0178] Aspect 42: An apparatus according to any one of Aspects 23 to 41, wherein comparing the DOF range associated with the scene with the corresponding one or more DOF ranges of each of the multiple cameras includes determining a numerical correspondence between the DOF range associated with the scene and the corresponding one or more DOF ranges of each of the multiple cameras.

[0179] Aspect 43: An apparatus according to any one of aspects 23 to 42, wherein determining the numerical correspondence comprises one or more of a percentage value, a ratio, a proportion, a function or a relationship.

[0180] Aspect 44: An apparatus according to any one of Aspects 23 to 43, wherein the processor is configured to: determine a first numerical correspondence between the DOF range associated with the scene and a first DOF range associated with a first camera among the multiple cameras; and determine a second numerical correspondence between the DOF range associated with the scene and a second DOF range associated with the first camera among the multiple cameras, wherein the second DOF range is different from the first DOF range.

[0181] Aspect 45: A non-transitory computer-readable storage medium having instructions stored thereon, the instructions, when executed by one or more processors, causing the one or more processors to perform any of the operations described in aspects 1 to 44.

[0182] Aspect 46: An apparatus comprising means for performing any of the operations described in aspects 1 to 44.

Claims

1. A method for selecting a camera from a plurality of cameras, the method comprising: Get an image from a camera; determining a depth of field (DOF) range associated with a scene captured in the image; comparing the DOF range associated with the scene captured in the image to one or more DOF ranges of corresponding cameras of a plurality of cameras; as well as A selected camera is determined from the plurality of cameras based on comparing the DOF range associated with the scene captured in the image to corresponding one or more DOF ranges of each camera in the plurality of cameras.

2. The method of claim 1 further comprising capturing additional images by the selected camera.

3. The method according to claim 2, further comprising: displaying the further image on a display; after displaying the further image on the display, obtaining a further DOF range different from the DOF range associated with the scene; determining an additional selected camera from the plurality of cameras that is different from the selected camera based on comparing the additional DOF ranges to the one or more DOF ranges of corresponding cameras from the plurality of cameras; as well as A third image is captured by the additional selected camera. The method of claim 3 , wherein the additional DOF range is obtained based on user input. The method of claim 1 , wherein the DOF range associated with the scene comprises a near distance associated with the scene and a far distance associated with the scene.

6. The method of claim 5, further comprising detecting a first subject in the scene captured in the image, wherein determining the DOF range associated with the scene captured in the image comprises determining at least one of a near distance associated with the first subject and a far distance associated with the first subject.

7. The method of claim 6, further comprising detecting a second subject in the scene captured in the image, wherein determining the DOF range associated with the scene captured in the image comprises determining the near distance associated with the first subject and the far distance associated with the second subject.

8. The method of claim 1 , wherein determining the DOF range associated with the scene captured in the image comprises: determining a close distance associated with the scene based on the image; as well as A long distance associated with the scene is determined based on the image.

9. The method of claim 1, wherein the DOF range associated with the scene is determined based at least in part on detecting one or more identified people, friends, pets, or objects from the image.

10. The method of claim 1, wherein a first camera of the plurality of cameras comprises a variable aperture having at least a first aperture size and a second aperture size, wherein a first DOF range of the first camera is associated with the first aperture size and a second DOF range different from the first DOF range is associated with the second aperture size. 11 . The method of claim 1 , wherein a first camera of the plurality of cameras is associated with a first DOF range and a second DOF range different from the first DOF range. 12 . The method of claim 11 , wherein the first DOF range is associated with a first aperture size and the second DOF range is associated with a second aperture size different from the first aperture size.

13. The method of claim 1, wherein the selected camera comprises the camera from which the image was obtained. The method of claim 1 , wherein the selected camera comprises a second camera different from the camera.

15. The method of claim 14, wherein the plurality of cameras does not include the camera from which the image was obtained.

16. The method of claim 1, wherein: a first DOF range associated with a first camera of the plurality of cameras comprising a first near distance and a first far distance; a second DOF range associated with a second camera of the plurality of cameras includes a second near distance and a second far distance; and The DOF range associated with the scene at least partially overlaps with both the first DOF range and the second DOF range.

17. An apparatus for selecting a camera from a plurality of cameras, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: Get an image from a camera; determining a depth of field (DOF) range associated with a scene captured in the image; comparing the DOF range associated with the scene captured in the image to one or more DOF ranges of corresponding cameras of a plurality of cameras; as well as A selected camera is determined from the plurality of cameras based on comparing the DOF range associated with the scene captured in the first image to corresponding one or more DOF ranges of each camera in the plurality of cameras.

18. The apparatus of claim 17, wherein the at least one processor is configured to capture additional images by the selected camera.

19. The apparatus of claim 17, wherein the DOF range associated with the scene comprises a near distance associated with the scene and a far distance associated with the scene.

20. An apparatus according to claim 19, wherein the at least one processor is configured to: detect a first subject in the scene captured in the image, wherein determining the DOF range associated with the scene captured in the image includes determining at least one of a close distance associated with the first subject and a long distance associated with the first subject.

21. An apparatus according to claim 20, wherein the at least one processor is configured to: detect a second subject in the scene captured in the first image, wherein determining the DOF range associated with the scene captured in the first image includes determining the near distance associated with the first subject and the far distance associated with the second subject.

22. The apparatus of claim 17, wherein the at least one processor is configured to: determining a close distance associated with the scene based on the image; and A long distance associated with the scene is determined based on the image.

23. The apparatus of claim 17, wherein the DOF range associated with the scene is determined based at least in part on detecting one or more identified people, friends, pets, or objects from the image.

24. The apparatus of claim 17, wherein a first camera of the plurality of cameras comprises a variable aperture having at least a first aperture size and a second aperture size, wherein a first DOF range of the first camera is associated with the first aperture size and a second DOF range different from the first DOF range is associated with the second aperture size.

25. The apparatus of claim 17, wherein a first camera of the plurality of cameras is associated with a first DOF range and a second DOF range different from the first DOF range.

26. The apparatus of claim 25, wherein the first DOF range is associated with a first aperture size and the second DOF range is associated with a second aperture size different from the first aperture size.

27. The apparatus of claim 17, wherein the selected camera comprises the camera from which the first image was obtained.

28. The apparatus of claim 17, wherein the selected camera comprises a second camera different from the camera.

29. The apparatus of claim 28, wherein the plurality of cameras does not include the camera from which the first image was obtained.

30. The apparatus of claim 17, wherein: a first DOF range associated with a first camera of the plurality of cameras comprising a first near distance and a first far distance; a second DOF range associated with a second camera of the plurality of cameras includes a second near distance and a second far distance; and The DOF range associated with the scene at least partially overlaps with both the first DOF range and the second DOF range.

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