Scene splitting method and device based on hybrid calculation mode

Abstract

The invention discloses a scene splitting method and device based on a hybrid calculation mode. The method comprises the following steps: acquiring physical scene information and optical scene information; obtaining a scene singular region according to the physical scene information; inputting the singular area of the scene into the image mapping matrix to obtain optical identification data; and splitting the original image according to the optical scene information and the optical identification data to obtain a splitting result. According to the invention, the problems that the image screening and splitting method in the prior art only analyzes and recognizes the whole image data, separates and processes the image data with problems and defects, and cannot perform high-efficiency splitting work on the image data with large flow and high complexity; and the image data processing efficiency and quality are reduced.

Description

Technical Field

[0001] The present invention relates to the field of image data scene processing, and in particular to a scene splitting method based on a hybrid computing method and a scene splitting device using the hybrid computing method. Background Art

[0002] With the continuous development of intelligent technology, people are increasingly using intelligent devices in their lives, work and study. The use of intelligent technology has improved the quality of people's lives and increased the efficiency of their study and work.

[0003] Currently, during the deployment and application of high-precision camera systems, image data is typically segmented based on the different types of image data captured by the high-precision cameras to determine which images can be processed and which images are discarded. However, existing image screening and segmentation methods only analyze and identify the overall image data, separating and processing problematic and defective image data. This method is unable to efficiently segment high-volume, highly complex image data, reducing the efficiency and quality of image data processing.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] In order to improve the above-mentioned technical problems, an embodiment of the technical solution of the first aspect of the present invention provides a scene splitting method and device based on a hybrid computing method, so as to at least solve the technical problem that the image screening and splitting methods in the prior art only analyze and identify the overall image data, and separate and process the problematic and defective image data, but cannot perform efficient splitting work on large-volume and high-complexity image data, thereby reducing the efficiency and quality of image data processing.

[0006] One aspect of an embodiment of the present invention provides a scene segmentation method based on a hybrid computing method, including obtaining physical scene information and optical scene information; obtaining a scene singular region based on the physical scene information; inputting the scene singular region into an image mapping matrix to obtain optical recognition data; and segmenting the original image based on the optical scene information and the optical recognition data to obtain a segmentation result.

[0007] The physical scene information comes from the physical scene, which refers to the environmental scene information of the original image, including geographic location information, object attribute information (geometric size, material, texture), etc.

[0008] Among them, optical recognition data refers to optical image data obtained through optical sensors (such as cameras, satellite remote sensing equipment, etc.), including texture data, spectral data, etc.

[0009] Among them, optical scene information refers to information related to optical images, including geographic location information, lighting information, shadow information, etc.

[0010] Optionally, the step of obtaining a scene singular region according to the physical scene information includes: obtaining size information and frame information in the physical field information; and generating a scene singular region according to a physical scene extraction equation, wherein the physical scene extraction equation includes:

[0011] l=a·fl+b

[0012] Wherein, 1 represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0013] Optionally, inputting the scene singular area into the image mapping matrix to obtain optical recognition data includes: extracting coordinate information of the scene singular area; inputting the coordinate information into the matrix

[0014]

[0015] , optical recognition data is obtained, wherein Z1~Zn represent n coordinate information of the scene singular area, and G1~Gn represent n optical recognition data mapped according to the coordinate information.

[0016] Optionally, the original image is split according to the optical scene information and the optical recognition data to obtain the splitting result, which includes: fusing the optical recognition data and the optical scene information to obtain a matching data group; generating splitting area information according to the matching data group and the original image; and performing a splitting operation on the original image according to the splitting area information to obtain a splitting result.

[0017] The purpose of integrating optical recognition data and optical scene information is to obtain a more comprehensive scene description.

[0018] The matching data set is used to find information that matches a specific area or feature in the original image by identifying key points in the original image and matching them with corresponding points in the optical scene information.

[0019] The split region information is used to divide the original image into multiple sub-regions, each corresponding to a different scene feature or physical object. Image segmentation is then performed using methods such as edge detection, allowing the split images to be used for further analysis.

[0020] In addition, for the purpose of refinement, the above steps can be repeated with the split image as the original image, namely, "obtaining physical scene information and optical scene information; obtaining scene singular areas based on the physical scene information; inputting the scene singular areas into the image mapping matrix to obtain optical recognition data; splitting the original image based on the optical scene information and optical recognition data to obtain the splitting results."

[0021] According to another aspect of an embodiment of the present invention, a scene segmentation device based on a hybrid calculation method is also provided, including: an acquisition module for acquiring physical scene information and optical scene information; an extraction module for obtaining a scene singular area based on the physical scene information; an input module for inputting the scene singular area into an image mapping matrix to obtain optical recognition data; and a segmentation module for segmenting the original image based on the optical scene information and the optical recognition data to obtain a segmentation result.

[0022] Optionally, the extraction module includes: an acquisition unit for acquiring size information and frame information in the physical field information; a generation unit for generating a scene singular region according to a physical scene extraction equation, wherein the physical scene equation includes:

[0023] l=a·fl+b

[0024] Wherein, 1 represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0025] Optionally, the input module includes: an extraction unit for extracting coordinate information of a singular area of ​​the scene; an input unit for inputting the coordinate information into a matrix

[0026]

[0027] , optical recognition data is obtained, wherein Z1~Zn represent n coordinate information of the scene singular area, and G1~Gn represent n optical recognition data mapped according to the coordinate information.

[0028] Optionally, the splitting module includes: a fusion unit for fusing the optical recognition data and the optical scene information to obtain a matching data group; a generation unit for generating splitting area information based on the matching data group and the original image; and a splitting unit for performing a splitting operation on the original image based on the splitting area information to obtain a splitting result.

[0029] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute a scene splitting method based on a hybrid computing method.

[0030] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein when the computer-readable instructions are executed, a scene splitting method based on a hybrid computing method is executed.

[0031] In an embodiment of the present invention, a method is adopted in which physical scene information and optical scene information are obtained; the physical scene information is extracted according to a physical scene extraction equation to obtain a scene singular region; the scene singular region is input into an image mapping matrix to obtain optical recognition data; and the original image is split according to the optical scene information and the optical recognition data to obtain a split result. This method solves the technical problem that the image screening and splitting methods in the prior art only analyze and identify the overall image data, separate and process the problematic and defective image data, and cannot perform efficient splitting work on large-volume and high-complexity image data, thereby reducing the efficiency and quality of image data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0033] Figure 1 is a flow chart of a scene splitting method based on a hybrid computing method according to an embodiment of the present invention;

[0034] Figure 2 is a structural block diagram of a scene splitting device based on a hybrid computing method according to an embodiment of the present invention;

[0035] Figure 3 is a block diagram of a terminal device for executing a method according to an embodiment of the present invention;

[0036] Figure 4 It is a storage unit for holding or carrying a program code for implementing a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0039] According to an embodiment of the present invention, a method embodiment of a scene splitting method based on a hybrid computing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0040] Example 1

[0041] Figure 1 is a flow chart of a scene splitting method based on a hybrid computing method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0042] Step S102: Acquire physical scene information and optical scene information.

[0043] Step S104: Obtain scene singular regions based on the physical scene information.

[0044] Step S106: inputting the scene singular region into the image mapping matrix to obtain optical recognition data.

[0045] Step S108 : Segment the original image according to the optical scene information and the optical recognition data to obtain a segmentation result.

[0046] Optionally, obtaining the scene singular region according to the physical scene information includes: obtaining size information and frame information in the physical field information; generating the scene singular region according to a physical scene extraction equation, wherein the physical scene equation includes: l=a·fl+b;

[0047] Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0048] Optionally, inputting the singular region of the scene into the image mapping matrix to obtain optical recognition data includes: extracting coordinate information of the singular region of the scene; inputting the coordinate information into the image mapping matrix to obtain optical recognition data; wherein, the image mapping matrix is Z1 to Zn represent n coordinate information of the scene singular area, and G1 to Gn represent n optical recognition data mapped according to the coordinate information.

[0049] Optionally, the original image is split according to the optical scene information and the optical recognition data to obtain the splitting result, which includes: fusing the optical recognition data and the optical scene information to obtain a matching data group; generating splitting area information according to the matching data group and the original image; and performing a splitting operation on the original image according to the splitting area information to obtain a splitting result.

[0050] Through the above embodiments, the technical problem that the image screening and splitting methods in the prior art only analyze and identify the overall image data, separate and process the problematic and defective image data, and are unable to efficiently split large-volume and highly complex image data, thereby reducing the efficiency and quality of image data processing, is solved.

[0051] In addition, the hybrid calculation in this embodiment refers to selecting different strategies for different scene information. For example, for physical scene information, a feature extraction strategy is adopted; for optical scene information, an information fusion strategy is adopted.

[0052] Example 2

[0053] Figure 2 is a structural block diagram of a scene splitting device based on a hybrid computing method according to an embodiment of the present invention, such as Figure 2 As shown, the device includes:

[0054] The acquisition module 20 is used to acquire physical scene information and optical scene information.

[0055] The extraction module 22 is used to obtain the scene singular area based on the physical scene information.

[0056] The input module 24 is used to input the scene singular area into the image mapping matrix to obtain optical recognition data.

[0057] The splitting module 26 is used to split the original image according to the optical scene information and the optical recognition data to obtain a splitting result.

[0058] Optionally, the extraction module includes: an acquisition unit for acquiring size information and frame information in the physical field information; a generation unit for generating a scene singular region according to a physical scene extraction equation, wherein the physical scene equation includes:

[0059] l=a·fl+b;

[0060] Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0061] Optionally, the input module includes: an extraction unit for extracting coordinate information of a singular area of ​​the scene; an input unit for inputting the coordinate information into an image mapping matrix to obtain optical recognition data; wherein the image mapping matrix is Z1 to Zn represent n coordinate information of the scene singular area, and G1 to Gn represent n optical recognition data mapped according to the coordinate information.

[0062] Optionally, the splitting module includes: a fusion unit for fusing the optical recognition data and the optical scene information to obtain a matching data group; a generation unit for generating splitting area information based on the matching data group and the original image; and a splitting unit for performing a splitting operation on the original image based on the splitting area information to obtain a splitting result.

[0063] Through the above embodiments, the technical problem that the image screening and splitting methods in the prior art only analyze and identify the overall image data, separate and process the problematic and defective image data, and are unable to efficiently split large-volume and highly complex image data, thereby reducing the efficiency and quality of image data processing, is solved.

[0064] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute a scene splitting method based on a hybrid computing method.

[0065] Specifically, the above method includes: obtaining physical scene information and optical scene information; extracting physical scene information according to a physical scene extraction equation to obtain a scene singular area; inputting the scene singular area into an image mapping matrix to obtain optical recognition data; and splitting the original image according to the optical scene information and the optical recognition data to obtain a splitting result.

[0066] Optionally, extracting physical scene information according to a physical scene extraction equation to obtain a scene singular region includes: obtaining size information and frame information in the physical field information; generating a scene singular region according to the physical scene extraction equation, wherein the physical scene equation includes: l=a·fl+b;

[0067] Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0068] Optionally, inputting the scene singular area into the image mapping matrix to obtain optical recognition data includes: extracting coordinate information of the scene singular area; inputting the coordinate information into the matrix , optical recognition data is obtained, wherein Z1~Zn represent n coordinate information of the scene singular area, and G1~Gn represent n optical recognition data mapped according to the coordinate information.

[0069] Optionally, the original image is split according to the optical scene information and the optical recognition data to obtain the splitting result, which includes: fusing the optical recognition data and the optical scene information to obtain a matching data group; generating splitting area information according to the matching data group and the original image; and performing a splitting operation on the original image according to the splitting area information to obtain a splitting result.

[0070] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein when the computer-readable instructions are executed, a scene splitting method based on a hybrid computing method is executed.

[0071] Specifically, the above method includes: obtaining physical scene information and optical scene information; extracting the physical scene information according to the physical scene extraction equation to obtain a scene singular region; inputting the scene singular region into the image mapping matrix to obtain optical recognition data; and splitting the original image according to the optical scene information and the optical recognition data to obtain a split result. Optionally, extracting the physical scene information according to the physical scene extraction equation to obtain the scene singular region includes: obtaining size information and frame information from the physical field information; generating the scene singular region according to the physical scene extraction equation, wherein the physical scene equation includes: l = a·fl+b

[0072] Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

[0073] Optionally, inputting the scene singular area into the image mapping matrix to obtain optical recognition data includes: extracting coordinate information of the scene singular area; inputting the coordinate information into the matrix , optical recognition data is obtained, wherein Z1~Zn represent n coordinate information of the scene singular area, and G1~Gn represent n optical recognition data mapped according to the coordinate information.

[0074] Optionally, the original image is split according to the optical scene information and the optical recognition data to obtain the splitting result, which includes: fusing the optical recognition data and the optical scene information to obtain a matching data group; generating splitting area information according to the matching data group and the original image; and performing a splitting operation on the original image according to the splitting area information to obtain a splitting result.

[0075] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0076] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0078] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0079] in addition, Figure 3 This is a schematic diagram of the hardware structure of a terminal device provided in one embodiment of the present application. Figure 3As shown, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. Communication bus 34 is used to implement communication between components. Memory 33 may include high-speed RAM memory or non-volatile storage (NVM), such as at least one disk storage device. Memory 33 may store various programs for performing various processing functions and implementing the method steps of this embodiment.

[0080] Optionally, the processor 31 may be implemented as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 via a wired or wireless connection.

[0081] Optionally, the input device 30 may include multiple input devices, such as a user interface for the user, a device interface for the device, a software programmable interface, a camera, and a sensor. Optionally, the device interface for the device may be a wired interface for data transmission between devices, or a hardware plug-in interface for data transmission between devices (such as a USB interface, a serial port, etc.); optionally, the user interface for the user may be, for example, a user-oriented control button, a voice input device for receiving voice input, and a touch sensing device for receiving user touch input (such as a touch screen or touchpad with touch sensing function); optionally, the software programmable interface may be, for example, an entry for users to edit or modify programs, such as an input pin interface or input interface of a chip; optionally, the transceiver may be a radio frequency transceiver chip with communication function, a baseband processing chip, and a transceiver antenna, etc. Audio input devices such as microphones may receive voice data. The output device 32 may include output devices such as a display and speakers.

[0082] In this embodiment, the processor of the terminal device includes functions for executing each module of the data processing device in each device. The specific functions and technical effects can be referred to the above embodiments and will not be repeated here.

[0083] Figure 4 A schematic diagram of the hardware structure of a terminal device provided in another embodiment of the present application. Figure 4 Yes Figure 3 A specific embodiment in the implementation process. Figure 4 As shown, the terminal device of this embodiment includes a processor 41 and a memory 42.

[0084] The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above embodiment.

[0085] Memory 42 is configured to store various types of data to support operations on the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, images, and videos. Memory 42 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0086] Optionally, the processor 41 is provided in the processing component 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input / output interface 47, and / or a sensor component 48. The specific components included in the terminal device are set according to actual needs and are not limited in this embodiment.

[0087] The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to complete all or part of the steps of the above-described method. Furthermore, the processing component 40 may include one or more modules to facilitate interaction between the processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate interaction between the multimedia component 45 and the processing component 40.

[0088] The power supply component 44 provides power to various components of the terminal device. The power supply component 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the terminal device.

[0089] The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0090] The audio component 46 is configured to output and / or input audio signals. For example, the audio component 46 includes a microphone (MIC), which is configured to receive external audio signals when the terminal device is in an operating mode, such as a voice recognition mode. The received audio signal can be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, the audio component 46 also includes a speaker for outputting audio signals.

[0091] The input / output interface 47 provides an interface between the processing component 40 and peripheral interface modules, such as click wheels, buttons, etc. These buttons may include, but are not limited to, volume buttons, start buttons, and lock buttons.

[0092] The sensor assembly 48 includes one or more sensors for providing various status assessments for the terminal device. For example, the sensor assembly 48 can detect the open / closed state of the terminal device, the relative positioning of components, and the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera, etc.

[0093] The communication component 43 is configured to facilitate wired or wireless communication between the terminal device and other devices. The terminal device can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card, allowing the terminal device to log into a GPRS network and establish communication with a server via the Internet.

[0094] From the above, we can see that Figure 4 The communication component 43, audio component 46, input / output interface 47, and sensor component 48 involved in the embodiment can all be used as Figure 3 Implementation of the input device in the embodiment.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0097] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0099] Throughout this specification, terms such as "one embodiment," "some embodiments," and "specific embodiments" mean that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0100] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0101] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these changes and modifications should all fall within the scope of protection of the claims of the present invention.

Claims

1. A scene splitting method based on hybrid computing, characterized in that: The steps include: Acquire physical scene information and optical scene information; Obtain the scene singular area based on the physical scene information; Inputting the singular region of the scene into an image mapping matrix to obtain optical recognition data; The original image is split according to the optical scene information and the optical recognition data to obtain a split result.

2. The scene segmentation method based on hybrid computing according to claim 1 is characterized in that ,The step of obtaining the scene singular area according to the physical scene information includes: Acquire size information and frame information from the physical field information; The scene singular region is generated according to the physical scene extraction equation, wherein the physical scene extraction equation is: l=a·fl+b: Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

3. The scene segmentation method based on hybrid computing according to claim 1, characterized in that: The step of inputting the scene singular area into an image mapping matrix to obtain optical recognition data comprises: Extracting coordinate information of the singular area of ​​the scene; Inputting the coordinate information into an image mapping matrix to obtain the optical recognition data; Among them, the image mapping matrix is Z1 to Zn represent n coordinate information of the scene singular area, and G1 to Gn represent n optical recognition data mapped according to the coordinate information.

4. The scene segmentation method based on hybrid computing according to claim 1, characterized in that: The step of splitting the original image according to the optical scene information and the optical recognition data to obtain a split result includes: fusing the optical recognition data and the optical scene information to obtain a matching data group; generating split region information according to the matching data group and the original image; The original image is split according to the split region information to obtain a split result.

5. A scene splitting device based on hybrid computing method, characterized in that: include: An acquisition module, used to acquire physical scene information and optical scene information; An extraction module, used to obtain the scene singular area based on physical scene information; An input module, configured to input the singular region of the scene into an image mapping matrix to obtain optical recognition data; The splitting module is used to split the original image according to the optical scene information and the optical recognition data to obtain a splitting result.

6. The device according to claim 5, characterized in that The extraction module includes: an acquiring unit, configured to acquire dimension information and frame information from the physical field information; A generating unit, configured to generate the scene singular region according to the physical scene extraction equation, wherein the physical scene equation is: l=a·fl+b; Wherein, l represents the scene singular region dataset, a represents the size information, b represents the frame information, and fl is the adaptation function of the scene singular region, wherein fl includes applying the feature function f(x,y), where x and y are the horizontal and vertical coordinates of the singular point.

7. The device according to claim 5, characterized in that The input module includes: An extraction unit, configured to extract coordinate information of a singular area of ​​the scene; An input unit, configured to input the coordinate information into an image mapping matrix to obtain the optical recognition data; Among them, the image mapping matrix is Z1 to Zn represent n coordinate information of the scene singular area, and G1 to Gn represent n optical recognition data mapped according to the coordinate information.

8. The device according to claim 5, characterized in that The splitting module includes: a fusion unit, configured to fuse the optical recognition data and the optical scene information to obtain a matching data set; a generating unit, configured to generate split region information according to the matching data group and the original image; The splitting unit is used to perform a splitting operation on the original image according to the splitting area information to obtain a splitting result.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method according to any one of claims 1 to 4.

10. An electronic device, characterized in that: The method comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method according to any one of claims 1 to 4 when executed.

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