243 results about "Pixel based" patented technology

Systems and techniques are described herein for foveated imaging. For instance, a method for foveated imaging is provided. The method may include determining a first region of interest within a first image based on a gaze of a user relative to the first image; clustering pixels of the first image into a one or more clusters of pixels based on a comparison of one or more characteristics of pixels of the first image; identifying a cluster of pixels from among the one or more clusters of pixels based on an intersection over union of the first region of interest and the cluster of pixels; generating a second region of interest based on the first region of interest and the cluster of pixels; and capturing a second image based on the second region of interest.

This application provides a calibration method, an infrared imaging device, and a storage medium for an infrared detector. The method includes: acquiring the original response values ​​of each pixel of the infrared detector; determining the current response value interval of each pixel based on the original response values ​​and the boundaries of multiple pre-calibrated response value intervals of the infrared detector, wherein multiple response value intervals are pre-calibrated based on the characteristics of the response curves of each integration channel of the infrared detector; acquiring the correction coefficient corresponding to the current response value interval of each pixel based on the pre-calibrated correction coefficients corresponding to each pixel in different response value intervals; correcting the original response values ​​of each pixel based on the correction coefficients; and outputting the corrected response values ​​of each pixel. This method effectively improves the non-uniformity correction effect of the image, especially in scenes with large temperature differences, and can significantly suppress horizontal stripe residue caused by nonlinear response.

This application discloses a method for constructing a dynamic risk map of a vehicle driving environment based on monocular vision, relating to the field of image recognition technology. It addresses the low accuracy and reliability of the final risk map output in existing monocular vision perception technologies due to phenomena such as target jitter, depth hovering, and repeated counting. The method analyzes the ground contact state, contact point coordinates, and foot depth of each target based on a reference support domain. It then projects, temporally fuses, and spatially deduplicates the contact point coordinates based on a projection strategy and foot depth to obtain a set of obstacle candidate points. Furthermore, it constructs a BEV occupancy grid map and a BEV risk grid map. Finally, it generates an image domain risk map based on pixel risk data and the BEV occupancy grid map, and constructs a cognitive risk map. The method outputs the cognitive risk map, the image domain risk map, and the BEV risk grid map, thereby improving the accuracy and reliability of the final risk map output.

The application relates to the field of infrared thermal imaging technology, and particularly provides an infrared thermal imaging AI intelligent temperature measurement method.The method comprises the following steps: image input and preprocessing, receiving infrared images and sunlight images photographed by an infrared thermal imager, classifying based on pixel value differences, and preprocessing to obtain binary images; image cutting, cutting the binary images, and identifying the boundaries of numbers and symbols; characteristic value calculation and extraction, dividing regions according to the boundaries of the numbers and symbols, and performing feature extraction to construct a characteristic vector; number classification and identification, comparing the characteristic vector with a pre-trained K-nearest neighbor algorithm (KNN) model to identify the numbers and symbols; and image number output, combining the identified numbers and symbols into complete temperature values, outputting the identification result, and storing the identification result for comparison diagnosis.The method automatically and accurately identifies temperature information in the infrared images, and realizes intelligent comparison diagnosis.

This invention relates to the field of computer graphics processing and 3D modeling technology, specifically to an automated intelligent generation system for 3D modeling of shoe exteriors. The system includes: data acquisition and streaming, where an image acquisition terminal generates and sends four-channel image data using a high-precision sensor array; feature reconstruction and representation, where a topological semantic reconstruction server extracts semantic features and constructs an initial geometric representation using an implicit function based on pixel alignment and a decoder to predict the symbolic distance function; topology optimization and segmentation, where topological streamline optimization and component semantic segmentation are performed based on the curvature field to generate a structured 3D model with independent component structures; and interaction and local updates, where an interactive terminal responds to user editing operations on independent component structures, sending local topology update commands, and the server performs parametric recalculation of the local mesh based on the commands. This invention solves the problem of the separation between geometric structure and semantic information, achieving real-time and refined model updates.

A light emitting display apparatus includes a display panel having pixels arranged therein and each of the pixels including a light emitting diode, a timing control portion including a degradation data calculation portion configured to calculate degradation data representing a degradation amount of the pixel based on input image data, and a first serial peripheral interface (SPI) communication portion configured to transmit and receive the degradation data through an SPI communication, a memory including a second SPI communication portion configured to perform the SPI communication with the first SPI communication portion, and storing the degradation data, and a driving power selection portion configured to selectively provide a first driving power and a second driving power lower than the first driving power to the first SPI communication portion. When the SPI communication is on, the second driving power is provided to the first SPI communication portion.

A video decoding method including reconstructing a current block comprising a current pixel via a prediction and an inverse transformation. The method including obtaining, from a bitstream, information indicating whether an in-loop filtering by using at least one pixel located outside a boundary of a current slice is applicable. The method including padding a value of the first adjacent pixel as a value of a second adjacent pixel. The method including determining filter coefficients corresponding to locations of third adjacent pixels used for an adaptive loop filter (ALF) of the current pixel based on a value of the current pixel and values of the third adjacent pixels. The method including obtaining a value of a filtered current pixel by adding the value of the current pixel.

A method of training a deep learning model includes blurring a plurality of wafer maps including a first target wafer map to provide a plurality of blurred wafer maps including a blurred first target wafer map, constructing first training data from the plurality of blurred wafer maps, based on a pixel-based similarity index, and pre-training the deep learning model based on the first training data, which include first positive and fist negative sets of wafer maps. The first positive set of wafer maps includes wafer maps of the plurality of blurred wafer maps, which are high in similarity with the blurred first target wafer map according to the pixel-based similarity index, and the first negative set of wafer maps includes wafer maps of the plurality of blurred wafer maps, which are low in similarity with the blurred first target wafer map according to the pixel-based similarity index.

The application discloses an image processing method, an electronic device and a storage medium. The method comprises the following steps: taking the center of gravity of a target object in a to-be-printed image as the center, filling the edge of the to-be-printed image to generate a filled image, the aspect ratio of the filled image being the same as a preset aspect ratio of a printing base, and the center of gravity of the target object being located at the center of the filled image; taking the center of gravity of the target object as the center, cutting the filled image to obtain a cut image with the preset aspect ratio, and the proportion of the target object in the cut image being greater than a preset proportion; extracting depth information of each pixel of the cut image, and determining printing height information of each pixel based on the depth information of each pixel. The shape of the filled image can be matched with the printing base, the base shape adaptability of the relief model can be improved, and the target object can be located at the center of the image obtained after printing.

A method of training a deep learning model includes blurring a plurality of wafer maps including a first target wafer map to provide a plurality of blurred wafer maps including a blurred first target wafer map, constructing first training data from the plurality of blurred wafer maps, based on a pixel-based similarity index, and pre-training the deep learning model based on the first training data, which include first positive and first negative sets of wafer maps. The first positive set of wafer maps includes wafer maps of the plurality of blurred wafer maps, which are high in similarity with the blurred first target wafer map according to the pixel-based similarity index, and the first negative set of wafer maps includes wafer maps of the plurality of blurred wafer maps, which are low in similarity with the blurred first target wafer map according to the pixel-based similarity index.

This application discloses a 3D imaging method and system based on structured light phase measurement, relating to the field of 3D imaging technology. The method includes: calibrating a structured light measurement system to obtain intrinsic and extrinsic parameters, and determining the effective measurement depth range and the corresponding effective absolute phase interval; controlling a projector to project a preset sinusoidal fringe pattern onto the measured scene, acquiring a deformed fringe image modulated by the surface of the measured object, and obtaining a wrapping phase map; calculating the initial fringe order of each pixel based on the effective measurement depth range using a frequency domain order-based fringe order no-neighborhood solution algorithm; performing no-neighborhood verification and error correction on the initial fringe order based on the effective absolute phase interval using an order self-verification and error correction algorithm to obtain the final fringe order; and calculating the absolute phase map of the entire field of view based on the wrapping phase map and the final fringe order, and converting the point cloud data. This application aims to solve the technical problem of existing spatial phase unfolding techniques relying on the continuity of adjacent pixels.

This application relates to an image processing method, apparatus, and computer device. The method includes: performing face recognition on a target image to obtain a target face region; determining target pixel information for each target pixel in the target face region based on reference pixel information of each reference pixel in a reference face region of each frame of the original image; for each target pixel, converting the target pixel information into color coordinates, and determining the weight of the target pixel based on the hierarchical relationship between the color coordinates and the range of human face coordinates; for each frame of the original image, determining the reference brightness of each reference pixel in the reference face region of the original image based on the weight of each target pixel and the original brightness of each reference pixel in the reference face region of the original image; and determining the current brightness of the target face region based on the reference brightness of each reference pixel in the reference face region. This method can improve the accuracy of face region brightness.

The application belongs to the technical field of optical three-dimensional measurement, and discloses a two-division iteration sub-pixel matching method and system for phase structured light three-dimensional measurement, which comprises the following steps: obtaining a phase map; performing legality marking and pixel-level initial matching on the phase map; determining a sub-pixel search area based on the pixel-level initial matching; and performing sub-pixel matching point search based on the sub-pixel search area by using a two-division iteration method to obtain a sub-pixel matching point. The application can improve the precision of binocular matching in phase structured light three-dimensional measurement, thereby improving the precision of three-dimensional reconstruction.

This invention discloses a kidney pathology image processing method integrating multi-tissue segmentation and quantitative lesion analysis, belonging to the field of medical image processing technology. The method includes the following steps: acquiring and preprocessing PAS-stained whole-slice images of kidney pathology; fine-tuning the segmentation model using an unsupervised domain adaptive strategy to address batch-to-batch staining differences; inputting the preprocessed image into a multi-class semantic segmentation neural network to obtain tissue segmentation results; training the network based on pixel-level annotations, employing a Class-Token mechanism, encoder-decoder architecture, and multi-scale feature fusion, and optimizing the Dice loss and binary cross-entropy loss based on joint weighting of categories and boundaries; performing statistical analysis based on the segmentation results and outputting quantitative analysis results. This invention provides an objective, reproducible, and intelligent auxiliary tool for the accurate assessment and large-scale clinical research of chronic kidney disease.

The invention belongs to the technical field of deep space exploration, and particularly relates to a multispectral image displacement depth adaptive mapping method and system based on radiometric calibration, and the method comprises the steps: obtaining original black field data with the input bit depth being N and original flat field data with the input bit depth being N; after bad pixels are shielded according to the original black field data and the original flat field data, row offset and row response are calculated; sequentially determining a global scale according to the row offset and the row response, calculating an integration slope, and generating a lookup table according to the integration slope; and acquiring real-time monitoring input, reading the M-bit output value corresponding to the real-time monitoring input according to the lookup table, and correcting and updating the lookup table according to the M-bit output value. Based on a parameterized calculation process (derivation from row bias and row response to a global scale and an integrated slope), different input bit depth and output bit depth requirements can be met by modifying input parameters, inherent limitation caused by hard coding is avoided, and the configurability of cross-platform tasks is improved.

This invention relates to the field of agricultural drone spraying technology, specifically to a method for detecting the spraying effect of agricultural drones based on visual monitoring. The invention acquires canopy images during the spraying process of the agricultural drone, then analyzes the airflow resistance parameters of pixels based on texture and optical flow to determine gap regions; analyzes the pixel-canopy scale coefficient to determine the flow capacity parameters of each pixel, thereby obtaining the airflow impedance coefficient of the pixel; then determines the airflow impact center and obtains the cumulative impedance map of airflow propagation; further, based on the distance between pixels in the gap region and the airflow impact center, and the pixel-canopy scale coefficient, obtains the pesticide penetration index. This invention introduces the pixel-canopy scale coefficient to analyze the visual physical scale of canopy gaps and the airflow resistance, thereby assessing the physical flow capacity of canopy gaps and simulating airflow propagation to assess the difficulty of pesticide penetration through the canopy, thus quantitatively evaluating the spraying effect of the agricultural drone.

This application discloses a target localization method, apparatus, electronic device, and storage medium. The method includes: acquiring an original image captured by a fisheye camera and ground reference pixels on the original image; performing semantic segmentation on the original image to obtain a semantic segmentation result for each target, and obtaining a set of candidate pixels for calculating the target's grounding pixel based on the semantic segmentation result; obtaining the target's grounding pixel based on the distance relationship between each candidate pixel in the candidate pixel set and the ground reference pixel; and obtaining the target's true grounding point based on the target's grounding pixel and the calibration parameters of the fisheye camera. The technical solution of this application can accurately identify the true grounding point of a target based on the original image from the fisheye camera.

This invention relates to the field of game video generation technology, and particularly to a method for real-time generation of interactive pixel-style game videos. The method includes the following steps: establishing a real-time interactive driving mechanism based on a diffusion transformer; establishing a memory consistency constraint mechanism based on a Transformer memory mapping module; constructing a pixel-style optimization module using an encoder and decoder; reconstructing pixel-style video frames using an end-to-end diffusion transformer architecture; and generating interactive pixel-style game videos in real-time based on these pixel-style video frames. This supports core scenarios such as real-time development and live interactive streaming of pixel-style games.

The application provides an image structured segmentation method, system, computer device and medium, and belongs to the field of image processing, comprising: obtaining an image to be segmented, and obtaining a preliminary segmentation result based on pixel color and spatial proximity; merging color blocks with color difference between adjacent color blocks less than a preset color merging threshold to obtain a preliminary merging result; at each scale, performing adjacent fusion on color blocks with color difference between adjacent color blocks in the image segmentation result at the previous scale less than a preset fusion threshold and the length of the circumscribed square less than the length of the circumscribed square threshold corresponding to the current scale to obtain the image segmentation result at the current scale, and determining the structured segmentation result of the image to be segmented according to the image segmentation results at multiple scales. The method can automatically generate and output a series of image segmentation results corresponding to different scale thresholds from fine granularity to coarse granularity, and realizes dynamic adjustment and on-demand selection of the segmentation granularity.

A method for detecting a number of tabs includes determining a pixel value of a first target pixel based on at least one pixel value of at least one first adjacent pixel adjacent to a first target pixel on a tab framework, and determining the number of tabs based on the pixel value of the first target pixel. Each of the at least one first adjacent pixel is an adjacent pixel of the first target pixel on a side in a first direction on the tab framework.

The invention discloses a complex-material-oriented multi-path full-frequency global illumination neural rendering method and system, and belongs to the technical field of computer graphics, and the method comprises the steps: carrying out the scene query of light emitted by pixels, and obtaining a primary geometric buffer and a multi-path mixed geometric buffer of the multi-time ejection of the light; respectively extracting main frequency features and multi-path sub-frequency features in a neural feature field based on the two features; performing directed graph structured modeling based on the multi-path sub-frequency features to obtain propagation features representing light path propagation information and global features representing overall light path information; performing multi-path frequency joint feature fusion based on the dominant frequency feature, the propagation feature and the global feature to generate a fusion feature with a full-frequency feature; and estimating a final radiation brightness value of the pixel based on the fusion feature, and completing neural rendering of global illumination. According to the method, efficient modeling and high-quality drawing of multi-path full-frequency global illumination under a complex material can be realized, and the sense of reality and dynamic consistency of a rendered image are remarkably improved.

This invention discloses a method for distinguishing and marking multiple foreground objects in image data streams. The steps include: 1) connecting a camera to an FPGA, whereby the camera transmits the acquired fiber optic unit array image to the FPGA line-by-line in real-time in a streaming manner; 2) the FPGA detects the received image pixels, identifies and marks the light spot pixels; 3) the FPGA calculates the optical centroid coordinates of the corresponding light spot pixels based on the marked light spot pixels. This invention can process the image data stream in real-time at the image acquisition end, distinguishing and marking each fiber optic light spot pixel. This allows for real-time calculation of the light spot centroid at the image acquisition end while maintaining accuracy, thereby greatly reducing data transmission volume and enabling the host computer to obtain the optical centroid coordinates of the fiber optic unit in real-time. This provides an accurate and real-time labeled image data stream for subsequent optical centroid calculation, thus improving overall positioning efficiency.

This invention discloses a machine learning-based image processing method for e-commerce scenarios, relating to the field of image processing technology. The method includes: constructing a polar coordinate system based on the original image of the e-commerce product, and performing pixel resampling on a local image of the e-commerce product based on the polar coordinate system to obtain an expanded annular image; obtaining candidate bridge enhancement images based on the expanded annular image; determining the number of neighboring skeleton pixels based on the candidate bridge enhancement images, and performing binary labeling on the skeleton images corresponding to the candidate bridge enhancement images to obtain a residual bridge topology image; obtaining a point set based on the residual bridge topology image, and constructing a residual bridge calibration region based on the point set. This invention improves the accuracy of the connection region localization results.

Systems and methods for automatically determining parasitic capacitance (PC) correction parameters for a Light Emitting Diode (LED) pixel. When a test trigger signal is received by a PC detector, the PC detector starts a test process to measure the PC correction parameters of the LED pixel coupled to the PC detector. The PC detector determines the PC correction parameter for the corresponding LED pixel based on the detected PC characteristics and stores the PC correction parameter of the LED pixel with respect to a location of the LED pixel in the LED array to facilitate centralized PC correction on the LED pixel based on the PC correction parameter retrieved based on the pixel location.

An environmental detection device is provided that is capable of determining the validity of parallax with high accuracy in various cases where parallax calculation is difficult. The environmental detection device includes an image acquisition unit 100 that acquires a first image and a second image; a feature set calculation unit 101 that obtains a feature set from each of the first and second images; a cost calculation unit 102 that determines the matching cost between the first and second images in a given search area based on the feature set; a parallax estimation unit 103 that estimates the parallax of a target pixel based on the matching cost; and a cost pattern storage unit 104 that stores a cost pattern indicating a pattern of matching costs.a score estimation unit 105, which calculates a score expressing the parallax uncertainty from the matching costs and the cost pattern in the search area; and a parallax cancellation unit 106, which determines the parallax to be cancelled from among the parallaxes estimated by the parallax estimation unit 103 by using the score.

This application discloses a structured light encoding and decoding method based on polarization state modulation, relating to the field of 3D reconstruction technology. The method includes: controlling a projector to sequentially project a group of polarized structured light patterns onto the object under test, and controlling a polarization camera to synchronously acquire these patterns, obtaining a corresponding polarization image sequence; calculating the polarization angle of each camera pixel based on the polarization image sequence, obtaining a polarization angle map for each frame; for each camera pixel, calculating the wrapping phase based on the polarization angle map, obtaining a wrapping phase map for that frequency; obtaining an absolute phase map based on the wrapping phase map using a single-frequency absolute phase unfolding algorithm, establishing a mapping relationship between the column coordinates of camera pixels and projector pixels; and, based on pre-calibrated intrinsic and extrinsic parameters of the camera and projector, combined with the linear mapping relationship, calculating the 3D point cloud of the object under test through triangulation. This application aims to address the shortcomings of 3D reconstruction results being affected by the reflectivity of the object surface and ambient lighting.

This application discloses a self-supervised multimodal structured light microscope reconstruction method and system, comprising the steps of: exciting a biological sample using structured light; obtaining J original fluorescence image sequences (Y) generated by the excitation of the biological sample, where J is an integer of 1 or more, each original fluorescence image sequence (Y) contains S fluorescence images, where S is an integer of 2 or more, each fluorescence image has a pixel size of M*N, and M and N are even numbers; generating a training set for each original fluorescence image sequence (Y) of the J original fluorescence image sequences (Y); training a denoising neural network based on the training set; and performing super-resolution reconstruction on the S fluorescence images in each original fluorescence image sequence (Y) of the J original fluorescence image sequences (Y) using a standard structured light super-resolution reconstruction algorithm to form a super-resolution image, and obtaining a final super-resolution reconstructed image by using the super-resolution image as input to the denoising neural network. [Selected Figure] Figure 1

This invention relates to the field of valve inspection technology, and discloses a method and system for detecting valve surface defects based on machine vision. The method includes: performing integrity verification on a valve visual image; if the integrity verification passes, the valve visual image is determined as the inspection image; performing curve fitting on all image pixels based on the image coordinate system; removing non-compliant image pixels based on the image curve; generating a detection code for the valve to be inspected based on the target image pixels of the target inspection image; when a suspected verification code is generated, determining a first detection score based on the number of region divisions, and determining a second detection score based on an association rule algorithm and the number of region divisions; determining a comprehensive detection score for the valve to be inspected based on the first and second detection scores; and determining whether the valve to be inspected has surface defects based on the comprehensive detection score. This invention ensures the reliability of valve surface defect detection.

Disclosed herein are systems and methods for creating virtual yield maps. A computing device collects one or more images from one or more satellites, the one or more images capturing at least a crop-containing plot, topography information for the crop-containing plot from one or more topographical sensors, and weather-related data for an area including the crop-containing plot. The computing device creates, based at least in part on information associated with the one or more images, a spectral index map. The computing device generates pixelized information for the crop-containing plot comprising a plurality of pixels representative of an equal portion of the crop-containing plot and includes a normalized value representative of crop yield for each respective pixel of the plurality of pixels based at least in part on the spectral index map, the topography information, and the weather-related data. The computing device creates a virtual yield map from the pixelized information.

Even if the distance between the imaging unit and the road surface changes due to changes in the weight of the moving object or changes in the air pressure of the wheels, calibration will be performed in accordance with these changes. [Solution] Based on the image captured by the imaging unit 210 regarding the stop line, which is a road surface feature having a known length along the direction of travel of the moving body MV, the movement amount detection unit 321A detects the amount of pixel movement. The identification unit 322A identifies the white line region in the captured image. Then, the calibration unit 323A, based on the amount of pixel movement and the identification result of the white line region in the captured image, as well as the known length of the stop line along the direction of travel of the moving body MV, determines that the moving body MV is traveling in a straight line at a constant speed on a flat road surface, and calibrates the distance on the road surface corresponding to one pixel, which is related to the distance between the imaging unit 210 and the road surface.