551 results about "High dynamic range" patented technology

In some embodiments, an electronic device generates an automatically-generated visual media using one or more recognized concepts extracted from a prompt inputted by a user. The recognized concepts include personalized template subjects and / or prompt suggestions. While displaying the user interface including the recognized concepts, the electronic device receives one or more inputs to modify the recognized concepts. The electronic device generates multiple variants of the automatically-generated visual content using the one or more recognized concepts. The electronic device adds an automatically-generated visual content to a content entry field of an application, different than the automatically-generated visual media application, without opening the automatically-generated visual media application. The electronic device applies a visual effect to content that is generated using an artificial intelligence model. The electronic device displays visual information corresponding to an artificial intelligence model. The electronic device displays an animation including displaying a user interface with high dynamic range luminance.

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention relates to the technical field of three-dimensional model design of molds, and provides a mold three-dimensional high-precision modeling method and system based on multi-modal data fusion, and the method comprises the steps: collecting an RGB image, a high dynamic range image and a structured light depth image of an entity mold, and carrying out the reconstruction to obtain a globally consistent dense point cloud; denoising and semantic segmentation are carried out on the point cloud, and a cavity function area, a core function area and a runner function area are recognized; performing differential parametric modeling based on an identification result: performing geometric primitive fitting on regular geometric features, performing NURBS curved surface reconstruction on a free-form surface, and performing swept volume or rotator fitting on a flow channel to obtain a parameterized geometry, a parameterized curved surface and a parameterized channel model; and applying boundary continuity constraint to fuse the parameterized geometry, the parameterized curved surface and the parameterized channel model, and constructing a hybrid parameterized three-dimensional model. According to the method, rapid reconstruction of a high-precision, high-semantic and full-parameterized three-dimensional model of an entity mold can be realized.

The invention discloses a die casting burr detection system and method based on machine vision, and relates to the field of machine vision detection.The method comprises the steps that based on a spectral feature vector set, a curved surface self-adaptive optical compensation mechanism is used, a polarization-spectral image set is obtained again through light path optimization, and the optimized polarization-spectral image set is obtained and corrected; generating a high-resolution image set, performing illumination distribution analysis on the surface of the die casting through the high-resolution image set to form a multi-exposure image set, inputting the multi-exposure image set into the constructed U-Net model for semantic segmentation, generating a high-dynamic-range image, and performing shadow elimination on the high-dynamic-range image by applying a gradient Poisson fusion algorithm to obtain a high-dynamic-range image. And after the shadow is eliminated, a pre-trained Mask-R-CNN model is used for detection, and a burr detection result is output. According to the method, the polarization-spectrum image set is optimized through curved surface adaptive optics, and imaging optimization and geometric correction of a complex curved surface area are achieved.

Disclosed in the present invention is a high-dynamic capacitive digital microphone system based on electrostatic force feedback. The system comprises an MEMS microphone and an interface circuit chip thereof, wherein the interface circuit chip comprises a charge pump, an analog front end, an analog-to-digital conversion circuit, an amplitude detection module, an amplitude adjustment control module and a pulse density modulation module. In the present invention, the characteristic of the sensitivity being able to be adjusted by means of changing a bias voltage by a capacitive sensor is used, and a charge pump with an adjustable output voltage is used to provide electrostatic force feedback, thereby further expanding the adjustable range of a front-end gain, and also reducing requirements for the noise performance of a readout circuit. Therefore, the system in the present application has the characteristics of high dynamic range and high-precision quantization.

The invention discloses an electric power cross-operation dynamic risk prediction and early warning method, system, equipment and medium based on multi-modal data fusion, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting multi-source data of a cross-operation site in real time, including personnel spatial position data, equipment state data, environmental parameters and operation flow information; performing multi-modal fusion processing on the multi-source data, and constructing space-time risk association features; constructing a dynamic risk factor matrix, performing fusion calculation on the static risk reference value and the dynamic correction value, and dynamically updating the weight of each factor along with time; predicting a risk change trend based on the combination of a time sequence prediction model and a graph structure modeling method; and generating a three-dimensional visual risk thermodynamic diagram based on a prediction result, and triggering a multi-level early warning mechanism when a risk value exceeds a threshold value. The image is reconstructed through multi-modal fusion processing, and key edge features can be recovered in complex environments such as low illumination, high dynamic range and fast motion.

The invention discloses an HDR video reconstruction method based on a standardized stream, and belongs to the technical field of high dynamic range image processing. The method comprises the following steps of: firstly, constructing a convolution optical flow estimation module with a self-adaptive normalized structure, wherein the convolution optical flow estimation module is used for accurately acquiring optical flow information between adjacent frames in an alternative exposure LDR video image sequence; then, carrying out multi-level feature alignment on the image sequence through an image alignment module so as to reduce alignment errors caused by illumination difference and movement; and finally, inputting the aligned and fused multi-level LDR image features into a standardized flow reconstruction network to realize high-quality HDR video image reconstruction. Aiming at the video reconstruction problem under the alternate exposure condition, the invention designs a standardized flow modeling structure considering the optical flow estimation precision and the feature alignment effect, and effectively improves the HDR video reconstruction quality in a complex dynamic scene.

The invention provides a dynamic target tracking method and device for an anti-drowning unmanned aerial vehicle and a storage medium, and the method comprises the steps: carrying out the preprocessing of water surface monitoring data collected by an RGB camera and an event camera which are carried on the unmanned aerial vehicle, and obtaining an event frame sequence and a water surface illumination quality evaluation result; performing water surface environment adaptability enhancement on the RGB image according to the event frame sequence and the water surface illumination quality evaluation result to obtain a time sequence registration relation and an enhanced RGB image sequence; performing underwater target feature fusion processing according to the enhanced RGB image sequence and the event frame sequence to obtain an underwater human body target feature template and an initial tracking position; and according to the underwater human body target feature template and the initial tracking position, carrying out continuous target tracking processing under a water surface shielding condition to obtain an underwater target tracking trajectory. According to the invention, the defect of the RGB camera in a complex water surface environment is compensated by using the high dynamic range characteristic of the event camera, and the target tracking precision and stability in an anti-drowning scene are improved.

The invention discloses an ultrahigh-definition panoramic image adaptive HDR fusion method based on data driving, and relates to the technical field of digital image processing, and the method comprises the steps: carrying out the coarse alignment and high dynamic range fusion of a low-resolution line graph sequence, and generating a low-resolution HDR panoramic image; carrying out sharpening processing on the low-resolution HDR panoramic image to obtain a low-resolution sharpened HDR image, and meanwhile, obtaining a multi-scale residual spectrum generated by sharpening; performing up-sampling on the multi-scale residual spectrum, and performing small-sample element learning fine tuning on the weight prediction network in combination with the low-resolution sharpened HDR graph to obtain a fine-tuned weight prediction network; performing weight prediction and layer-by-layer fusion on the registered high-resolution tile sequence on a multi-scale pyramid based on a fine tuning weight prediction network model; according to the method, the sharpening processing is implemented on the low-resolution HDR panorama, and the multi-scale residual spectrum is extracted, so that the detail sensitivity and adaptability in a complex scene are improved.

The invention belongs to the technical field of semiconductor display, particularly relates to a backlight and display drive cooperative control LED module system, and aims to solve the problems of brightness distortion, halo effect and energy efficiency degradation caused by disconnection of backlight and pixel control in high dynamic range display. The system generates adaptive backlight distribution through image semantic analysis and illumination field reconstruction, and realizes stable light output in combination with dynamic cutting compensation and temperature feedback; and the display driving end performs gray scale feedforward correction according to the backlight state, and introduces a cross-domain error back propagation mechanism to realize frame-level online calibration. And the dynamic response and the processing real-time performance are further optimized through motion vector sensing and double-phase time sequence scheduling. According to the system, the contrast ratio is increased to more than 100000: 1, the halo radius is reduced to 40% of that of a traditional scheme, the power consumption is reduced by 18%, and the visual experience and the operation stability are remarkably improved.

The invention discloses a tunnel face geological information acquisition and image processing method based on machine vision, which belongs to the technical field of image processing, and comprises the following steps: resolving the pose of a terminal in real time through a visual inertial odometer, generating augmented reality guide information, and guiding the terminal to move to a standard acquisition point; at each acquisition point, performing multi-stage verification of scene semantic compliance, motion blur and defocus and illumination uniformity on the image flow to ensure the acquisition quality; for tunnel high-dynamic illumination, adopting an adaptive brightness segmentation and multi-resolution fusion technology based on an improved Otsu algorithm to perform high-dynamic range reconstruction on a single-frame image so as to recover details; and finally, performing clustering optimization based on the feature matching similarity, and screening out an optimal image subset for three-dimensional reconstruction. According to the method, the problems of subjective and random acquisition process, many image quality defects, detail loss under extreme illumination and large image data redundancy are solved.

A system, method, and computer program product are provided for high-dynamic range images. In use, a first pixel attribute of a first pixel is received and a second pixel attribute of a second pixel is received. Next, a scalar based on the first pixel attribute and the second pixel attribute is identified. Finally, the first pixel and the second pixel are blended, based on the scalar, wherein the first pixel is brighter than the second pixel. Additional systems, methods, and computer program products are also presented.

A system, method, and computer program product are provided for high-dynamic range images. In use, a first pixel attribute of a first pixel is received and a second pixel attribute of a second pixel is received. Next, a scalar based on the first pixel attribute and the second pixel attribute is identified. Finally, the first pixel and the second pixel are blended, based on the scalar, wherein the first pixel is brighter than the second pixel. Additional systems, methods, and computer program products are also presented.

The invention belongs to the technical field of electromagnetic detection, and relates to an electromagnetic detection multi-level current emission system for a plateau environment and a control method. The control method comprises the steps of calculating load model impedance; analyzing an MFPWM control process, and determining an optimal control parameter by using an optimization target of transmitting spectrum intensity maximization; and MFPWM optimization control: realizing directional power distribution and independent regulation and control of each frequency component by adjusting the frequency and amplitude of each modulation signal component. A multi-level topological structure is adopted, high output voltage is achieved while switching voltage stress is reduced, and the problems that cost rises and system complexity is too high due to the fact that a traditional H-bridge inverter depends on high power supply voltage are solved; by matching with multi-frequency pulse width modulation optimization control, spectrum energy can be flexibly configured, high-frequency components of transmitted signals are enhanced, and the spectrum energy utilization rate and the signal-to-noise ratio are improved; grounding impedance changes in a high dynamic range can be accurately tracked, and the requirements of deep geological electromagnetic detection in an extreme environment are met.

The invention discloses a real-time monitoring and analyzing system based on video intelligent analysis, and relates to the technical field of intelligent video surveillance. During operation of the system, video information and environmental parameters in a monitoring area are synchronously acquired, and continuous video streams are acquired based on a high dynamic range image sensor; time information and space position information are embedded in the video frame to form a data source with space-time attributes; the method comprises the steps of performing real-time preprocessing and feature extraction on a video stream, completing noise suppression, background modeling and target extraction, performing cross-view association and trajectory fusion on multi-node target features, realizing space-time alignment analysis, sensing scene and metadata changes, dynamically adjusting analysis parameters, and executing online updating of a model; semantic analysis and state judgment are carried out on the fusion features, and graded alarm information is output; operation data is stored in a centralized mode, optimization retraining is conducted on the model based on historical data, and synchronization is conducted to the edge side to improve the overall performance of the system.

A method is provided for use in a sensor, comprising: generating a sensing signal by using one or more sensing elements; amplifying the sensing signal by using a first gain to produce, at least in part, a first amplified signal, the first amplified signal having a first offset; amplifying the sensing signal by using a second gain to produce, at least in part, a second amplified signal, the second amplified signal having a second offset; generating an adjusted signal based on the first amplified signal, the second amplified signal, the first gain, and the second gain, the adjusted signal approximating a difference between the second amplified signal and an offset of the second amplified signal; and using the adjusted signal to generate an output of the sensor.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

To provide a method of capturing an image having a high dynamic range.SOLUTION: A camera having at least one image sensor is disclosed. The image sensor includes a pixel array comprising narrowband pixels and wideband pixels of at least three colors. The camera is operable in a first mode in which the image sensor has first gain settings and generates an output having a first response. Further, the camera is operable in a second mode in which the image sensor has second gain settings and generates an output having a second response. The image sensor is configured to generate, in the first mode, a digital output in which the wideband pixels have a spectral output response approximately equal to a sum of spectral output responses of the narrowband pixels. The image sensor is further configured to generate, in the second mode, a digital output in which the wideband pixels have a spectral output response greater than in the first mode.SELECTED DRAWING: Figure 1

A system, method, and computer program product are provided for high-dynamic range images. In use, a first pixel attribute of a first pixel is received and a second pixel attribute of a second pixel is received. Next, a scalar based on the first pixel attribute and the second pixel attribute is identified. Finally, the first pixel and the second pixel are blended, based on the scalar, wherein the first pixel is brighter than the second pixel. Additional systems, methods, and computer program products are also presented.

A system, for measuring a dielectric property of a medium, includes: a resonator; an oscillator electrically coupled to the resonator and configured to generate an electrical signal representing an oscillation of the oscillator when the resonator is disposed in a vicinity of the medium; and measuring circuitry that receives the electrical signal. The measuring circuitry determines, from the electrical signal, at least one of a resonance frequency of the resonator and a quality factor of the resonator. The measuring circuitry determines, from the at least one of the resonance frequency and the quality factor, the dielectric property of the medium.

A three-dimensional computer-generated hologram reconstruction method based on fixation area optimization belongs to the field of hologram reconstruction, and the iterative optimization process is as follows: firstly constructing a weight matrix, then carrying out light field propagation, then carrying out target image generation and loss function construction, then carrying out gradient optimization and parameter updating, and when a preset requirement is met, stopping the optimization process. And finally, loading the hologram. The process of the end-to-end neural network comprises the following steps: firstly, generating a middle plane complex amplitude light field from an input image through a complex amplitude field generation module; collecting an eye image, and extracting coordinates and related features of a sight fixation point of the user; then, the intermediate plane complex amplitude light field is subjected to light field propagation to obtain an initial hologram, and the initial hologram is fused with sight line features to enhance the weight of a fixation area; and finally, directly outputting a pure phase hologram capable of performing phase modulation on an incident light field. The invention further provides a three-dimensional computer-generated hologram reconstruction device. According to the invention, the human eye gazing area is emphatically optimized, and holographic near-to-eye display in a high dynamic range is realized.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

A distance image processing unit performs pre-measurement by a driving method that allows calculation of a distance and a reflectivity of a subject present in a measurement space, calculates the distance to the subject and the reflectivity of the subject as a situation of the subject present in the measurement space based on a measurement result of the pre-measurement, performs main measurement based on the situation of the subject present in the measurement space, and selects, in the main measurement, whether to perform high dynamic range (HDR) driving that expands the measurement range or to perform normal driving that does not expand the measurement range, based on the situation.

Multiple images that include a first image, a second image, and a third image are received. The multiple images are such that the second image is temporally between the first image and the third image. The first image, the second image, and the third image are combined to obtain a long-exposure image. High dynamic range processing is applied to the second image and the long-exposure image to obtain an output image with a larger dynamic range than a dynamic range of the second image. The high dynamic range processing uses the second image as a short-exposure image. An image based on the output image is transmitted, stored, or displayed.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.

Systems and methods for multi-modal subcellular segmentation using photolysable biomarkers and / or transcriptomic readout density maps are disclosed. The systems and methods improve the accuracy of cell segmentation of the nucleus, cytoplasm, and cell membrane regions by using optical and bleach correction from a variety of photolysable morphological markers in combination with high quality 3D images acquired with high dynamic range scans and spatial transcriptomic readout density maps.

Methods and systems for generating trim-pass metadata for high dynamic range (HDR) video are described. The trim-pass prediction pipeline includes a feature extraction network followed by a fully connected network which maps extracted features to trim-pass values. In a first architecture, the feature extraction network is based on four cascaded convolutional networks. In a second architecture, the feature extraction network is based on a modified MobileNetV3 neural network. In both architectures, the fully connected network is formed by a set of three linear networks, each set customized to best match its corresponding feature extraction network.

The invention relates to the technical field of intelligent robot vision, and discloses a light environment image evaluation system based on a bionic robot, which comprises an image acquisition module, a light environment feature extraction module, a self-adaptive image processing pipeline, a quality evaluation feedback loop and a low-delay hardware acceleration architecture, the image acquisition module consists of a visible light camera, a local brightness sensor and a spectral analysis sensor, and adopts a partition photosensitive design; the light environment feature extraction module is used for establishing a three-dimensional feature space of illumination intensity-color temperature-time; the adaptive image processing pipeline is used for dynamically selecting an optimal image processing mode according to light environment characteristics and reconstructing the image; the quality evaluation feedback loop is used for evaluating the quality of the processed image and dynamically adjusting evaluation parameters; the low-latency hardware acceleration architecture is used for accelerating the data processing speed of a specific function. The adaptive capacity of the system in a dynamic light environment is improved, the image quality stability is improved, and effective processing of a high dynamic range scene is realized.

An example method includes obtaining a plurality of images of at least a portion of a semiconductor workpiece. Each of the plurality of images associated with exposing the at least a portion of the semiconductor workpiece to radiation at a different radiation intensity. The example method includes generating a composite workpiece image of at least a portion of the semiconductor workpiece based at least in part on the plurality of images.