1113 results about "Pixel pair" patented technology

In implementation of techniques for three-dimensional reconstructions based on Gaussian primitives, a computing device implements a reconstruction system to receive a first digital image depicting an object from a first angle and a second digital image depicting the object from a second angle. The reconstruction system segments the first digital image and the second digital image into patches. The reconstruction system then generates, using a machine learning model, three-dimensional Gaussian primitives that predict parameters of points of the object in a three-dimensional space that correspond on a per-pixel basis to pixels of the patches. The reconstruction system then forms a three-dimensional reconstruction of the object for display in a user interface by merging the three-dimensional Gaussian primitives.

The invention discloses a shock wave overpressure field global measurement method based on multi-view image fusion, belongs to the technical field of explosive shock wave measurement, and is suitable for weapon equipment power evaluation and blasting safety analysis. The method comprises the following steps of: synchronously acquiring a time sequence image of the whole explosion process through a distributed multi-view high-speed imaging system, preprocessing the image by adopting pixel-by-pixel comparison, square operation enhancement and normalization processing, and positioning a shock wave edge contour; the blasting center coordinate is positioned through binocular parallax, the shock wave initial radius is determined by combining spatial domain analysis, and the key feature points of the front and rear edges of the wavefront are detected by using a dynamic search window and a radial gradient field. Based on multi-view constraints, a three-dimensional point cloud is generated through direct linear triangulation, and a three-dimensional wave front form is reconstructed in combination with least square spherical fitting. And finally, constructing a wavefront radius-time evolution model, and deducing a quantitative relationship between the instantaneous propagation velocity and the peak overpressure in combination with a Ranki ne-Huton iot relationship, thereby realizing the global high-precision calculation of the overpressure field.

The invention discloses a display panel and a driving method thereof, the display panel comprises a plurality of first switches and a plurality of second switches, N columns of pixels are arranged in a first display area, and M columns of pixels are arranged in a second display area; the first scanning line is connected with N columns of pixels in the first display area and M columns of pixels in the second display area, the second scanning line is connected with the N columns of pixels in the first display area, and each first switch is connected with two data lines corresponding to sub-pixels with the same color in the first display area and the second display area; each second switch is connected with a data driving chip and a data line corresponding to a pixel in the first display area; wherein N and M are natural numbers greater than or equal to 2. The two data lines corresponding to the sub-pixels with the same color in the first display area and the second display area respectively receive the same data signal or different data signals to realize display in different display modes by switching on or switching off the first switch and the second switch, so that insufficient charging under a high refresh rate is improved.

The invention discloses a high-load data steganography method, system and device and a medium, and the method comprises the steps: obtaining secret data of a power system, and converting the secret data into a novenary sequence; a quantum key distribution system is utilized to generate a shared key, the novenary sequence is encrypted through an encryption key, encrypted information is obtained, and the shared key comprises the encryption key and a mapping key; determining a corresponding first unit matrix from a preset mapping library based on the mapping key, and performing non-overlapping tiling on the first unit matrix in a two-dimensional plane to obtain a second-order reference matrix; and scanning a preset carrier image into a non-overlapping first pixel pair sequence according to a Hilbert curve, and embedding the encrypted information into the first pixel pair sequence by using the second-order reference matrix to obtain a steganographic image, so that the data transmission security can be improved.

The invention relates to the technical field of image processing, in particular to a food production line cleanliness monitoring method and system based on machine vision. The method comprises the steps of obtaining a Lab image of a to-be-detected conveyor belt of a food production line, determining an abnormal score value of a target pixel point in the Lab image, and constructing a data item edge weight of an energy function according to the abnormal score value of the pixel point in the Lab image; determining a periodic displacement amount of a periodic structure of the Lab image in the motion direction of the conveyor belt to be detected, and determining a periodic consistency factor of adjacent pixel pairs according to the periodic displacement amount; according to the method, a data item edge weight of an energy function is established, a smooth item edge weight of the energy function is established according to texture features and a period consistency factor, the energy function is established by using the data item edge weight and the smooth item edge weight, segmentation enabling the energy function to be minimized is performed on the Lab image, and a cleanliness monitoring result can be determined according to the minimum value of the energy function. According to the technical scheme, the cleanliness of the conveying belt of the food production line can be automatically monitored.

The invention discloses an ultra-low illumination-oriented SPAD active imaging data compression method, which comprises the following steps of: performing wavelet decomposition on histogram data of a single pixel in a time-frequency domain based on wavelet transform to obtain low-frequency data of each pixel after wavelet decomposition; by taking each pixel as a center, performing non-maximum suppression and data enhancement on the low-frequency data after the wavelet decomposition of the current pixel by using adjacent pixels to obtain processed compressed data; customizing different Gaussian kernel parameters for each pixel according to the possibility that each pixel is located at the boundary, and performing Gaussian filtering on the processed compressed data of each pixel to obtain filtered data; and performing depth estimation on the filtered data to obtain a final depth image. According to the method, the depth reconstruction performance of the laser pulse can be improved by utilizing the multi-resolution characteristic of wavelet transform, the space-time correlation of signal photons and the smoothness of Gaussian filtering.

The invention discloses a multi-modal ultrasonic image intelligent analysis system based on deep learning, particularly relates to the field of ultrasonic image intelligent analysis, and is used for solving the problems of speckle noise and displacement artifacts caused by sound beam attenuation during deep abdomen scanning of a bedside ultrasonic person with a relatively high in-vivo quality index. Partition noise reduction is guided through pixel noise distribution associated with depth and attenuation, and deep micro-textures are reserved; the color flow velocity and the elastic displacement are anchored through the noise reduction gray scale edge, three-mode sub-pixel alignment is completed, and cross-channel deformation and drift are eliminated; the self-attention fusion network synchronously extracts texture, speed and hardness features on the unified alignment surface to realize coupling of a complete biological structure and dynamic information; the texture residual error rate and the flow rate matching degree form complementary evaluation, noise distribution and alignment grid real-time self-adjustment are driven, frame-by-frame iteration convergence is achieved, and image definition and feature consistency are continuously improved.

The invention provides a pallet pose estimation method and system, and the method comprises the steps: obtaining a color image which comprises a pallet region and at least part of pixels of which are aligned, and point cloud data, and cutting the color image to obtain a target image which only comprises the pallet region; obtaining a point cloud of an area corresponding to the target image from the point cloud data according to an alignment relationship between the color image and the point cloud data, and obtaining a to-be-fitted point cloud; performing plane fitting on the to-be-fitted point cloud to obtain a plane fitting model; angle information and center coordinates of the pallet are calculated based on the plane fitting model and a preset reference coordinate system, and the angle information and the center coordinates of the pallet are utilized to construct a pose matrix of the pallet to obtain pose information of the pallet. The pallet pose estimation method provided by the invention has higher robustness and higher accuracy.

A method includes obtaining a medical image and receiving a selection of both a template image and a region of interest within the template image. The method includes inputting both the medical image and the template image into a trained vision transformer model and outputting from the trained vision transformer model both pixel level feature vectors from the medical image and a reference pixel level feature vector from the region of interest of the template image. The method includes inputting both the pixel level feature vectors and the reference pixel level feature vector into a trained contrastive similarity metric learning model and outputting from the trained contrastive similarity metric learning model pixel that are similar to reference pixels. The method includes labeling the pixels in the medical image with a segmentation mask, wherein the pixels that are labeled in the medical image correspond to the region of interest.

The invention discloses an electronic component packaging defect detection method and system based on image acquisition, and relates to the field of image analysis, and the method comprises the steps: collecting a multi-mode image of a to-be-detected electronic component package; pixel alignment is carried out on the multi-modal image, the multi-modal image after pixel alignment is used as an R channel, a G channel and a B channel to be stacked, and a three-channel pseudo-color image is generated; inputting the three-channel pseudo-color image into a pre-trained convolutional auto-encoder model to obtain a reconstructed three-channel image; analyzing the difference between the three-channel pseudo-color image and the reconstructed three-channel image to obtain an analysis result, and generating a three-channel residual image according to the analysis result; generating a single-channel defect saliency map based on the three-channel residual image; and when a connected region of which the pixel value exceeds a preset pixel threshold value exists in the single-channel defect saliency map, judging that the connected region is a real defect. The detection false alarm rate can be effectively reduced, and the production efficiency is improved.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The embodiment of the invention provides a check image correction method, device and equipment. The method comprises the following steps: acquiring an original deformation check image; processing the original deformation check image through a backbone network, a distortion encoder, a correction decoder and an output predictor in an image correction model in sequence to obtain a target pixel displacement field representing a pixel corresponding relation from the original deformation check image to a corrected check image; and according to the target pixel displacement field, performing bilinear difference distortion operation on the original deformed check image to obtain a corrected check image after the original deformed check image is corrected. An original deformed check image is encoded, an encoded feature map is learned, a corresponding pixel displacement field is generated by a correction decoder, and pixels are sampled from the original deformed check image using the predicted pixel displacement field to reconstruct a corrected check image. Automatic and high-quality deformation check image automatic correction is realized, the labor cost is reduced, and the correction accuracy is improved.

The invention discloses a microscopic image integer pixel alignment method based on sub-pixel edge offset detection, and relates to the field of microscopic image processing. The problems that an existing sub-pixel registration technology depends on iterative optimization or a complex deformation model, and real-time processing is difficult; sensor sampling errors and mechanical translation errors need to be compensated through a high-resolution camera or a precise displacement table, and the system cost is high. According to the invention, through sub-pixel-level offset compensation, sub-pixel alignment can be realized without depending on a high-precision displacement table, and the hardware cost is remarkably reduced. The method comprises the following steps: extracting image edge feature points by using an edge detection algorithm, fitting a gray centroid to calculate a sub-pixel offset, and counting an average offset; generating an affine transformation matrix according to the average offset, resampling the image, and aligning pixel values to an integer coordinate grid; and finally, cutting the image edge according to the interpolation algorithm type to eliminate the ghost. The method is also suitable for the application fields of fluorescence microscope multi-view splicing, super-resolution reconstruction and the like.

The invention provides a super-resolution binocular image generation method and system based on geometric structure consistency, and the method comprises the steps: extracting the deep features of a low-resolution binocular image through employing a convolutional neural network or a Transform model, achieving the information interaction of a left image and a right image in combination with a cross attention module, and constructing a pixel incidence matrix of the left image and the right image; acquiring a pixel corresponding relation of the left image and the right image by using the pixel incidence matrix, and constructing a continuous parallax field; performing spatial warping on the deep features based on a continuous parallax field to obtain warping features, and aligning the deep features of the left and right images; and merging the deep features and the warping features after spatial alignment, inputting the merged features into a feature up-sampling module based on implicit two-dimensional expression, and outputting a high-resolution binocular image of the same scene. The invention provides a binocular image super-resolution technology comprising binocular image feature extraction, continuous parallax field construction based on implicit two-dimensional expression, left and right image feature space alignment and feature upsampling based on implicit two-dimensional expression.

A cross-regional and cross-view learning (C2RV) framework is provided for sparse-view reconstruction in cone-beam computed tomography (CBCT) by advantageously leveraging cross-region and cross-view feature learning to enhance representation of a point in 3D space before estimating an attenuation coefficient of the point. Specifically, multi-scale 3D volumetric representations (MS-3DV) are first introduced, where features are obtained by back-projecting multi-view features at different scales to the 3D space. Explicit MS-3DV enable cross-regional learning in the 3D space, providing richer information that helps better identify different internal anatomy structures. Hence, features of the point can be queried in a hybrid way, i.e. multi-scale voxel-aligned features from MS-3DV and multi-view pixel-aligned features from projections. Instead of considering queried features equally, scale-view cross-attention (SVC-Att) is used to adaptively learn aggregation weights by self-attention and cross-attention. Finally, multi-scale and multi-view features are aggregated to estimate the attenuation coefficient.

The invention discloses a hyperspectral image particle segmentation method and device, relates to the field of image processing, and is used for improving the accuracy of particle segmentation according to a hyperspectral image. The method comprises the steps that a local area corresponding to pixels is selected, a preset matrix of the pixels is constructed, the preset matrix is formed by covariance of any two spectral vectors in spectral vectors of the pixels in the local area corresponding to the pixels, and whether the pixels are candidate edge pixels or not is judged according to the characteristic value condition of the preset matrix; compared with the existing edge detection method which only utilizes the spatial gradient information of the image, the method has the advantages that the accuracy of edge pixel detection can be improved by judging whether the pixel is the candidate edge pixel or not through combining the characteristics of the spatial dimension and the spectral dimension; and the particle region is further segmented according to the candidate edge pixels, so that the edge pixels are detected by combining the features of the spatial dimension and the spectral dimension and the particle region is segmented according to the edge pixels, and the accuracy of performing particle segmentation according to the hyperspectral image can be improved.

The invention provides a hyperspectral image classification enhancement method based on adaptive multi-scale superpixel constraint. The method comprises the following steps: establishing a main path and an auxiliary path; generating density information of an input feature map for the auxiliary path, and determining the number of superpixels; after the input feature map is compressed, smooth feature maps with different scales are generated; generating a corresponding number of superpixels in a corresponding kernel scale space according to the smooth feature map; for kernel scales, aggregating feature mean values in each superpixel region to obtain superpixel features of each kernel scale; calculating the weight of the corresponding kernel scale; obtaining a multi-scale super-pixel fusion feature map by using the weights and the aligned multi-scale features; total loss is constructed based on loss functions of the two classification paths; and training a neural network model of a main path and an auxiliary path by taking the neural network model as an optimization target to obtain an enhanced classification model for hyperspectral image classification. The method is compatible with an existing hyperspectral image classification network, and a lightweight enhancement scheme is provided for a deployed model.

The invention provides an image registration method and an image registration device, which are applied to the technical field of image processing. Comprising the following steps: acquiring a target reference image and a first target to-be-registered image of a to-be-detected area containing FOD; constructing an optimal affine transformation model according to the target reference image and the N preset pitch angles, and further performing linear transformation on the first target to-be-registered image to obtain a second target to-be-registered image; according to a Gaussian difference pyramid of a second target to-be-registered image, screening an extreme point of which the principal curvature does not exceed a preset principal curvature threshold as a first feature point, and further screening a first feature point of which the re-projection error and the sampling probability meet a preset condition as a second feature point; and solving a homogeneous linear equation set of the pixel point pairs matched with the second feature points by adopting an SVD method to obtain a transformation homography matrix, and then registering the first target to-be-registered image to obtain a target image. The contradictory closed loop of low efficiency-insufficient precision is broken through, and the aviation operation safety is guaranteed.

The invention discloses a time domain point-by-point imaging method, system and device for receiving and transmitting synthetic aperture sonar, and the method comprises the steps: calculating the synthetic aperture length and synthetic aperture time for any pixel in an imaging region; calculating the distance between the sonar and the pixel at the moment of transmitting the signal, and calculating the distance between the sonar and the pixel at the moment of receiving the signal to obtain an accurate two-way slant range process; calculating a reference echo signal corresponding to the pixel according to a two-way slant range process and related parameters of the signal; carrying out range-to-frequency domain conversion on the actually measured echo signal, carrying out range-to-frequency domain conversion on the reference echo signal, and carrying out conjugate multiplication processing on a conversion result; coherent accumulation is carried out on all conjugate multiplication results, and the result is used as an imaging result of the pixel; and for other pixels in the imaging area, repeatedly executing the steps to obtain imaging results of all pixels in the whole imaging area.

The invention provides a wafer state detection device and method, and the device comprises a light source which is used for irradiating a to-be-detected wafer, and a turning light path which is used for guiding the reflected light of a left region, a central region and a right region of the to-be-detected wafer to an imaging module. And the processing module is used for determining the number of overlapped wafers according to the upper and lower edge pixel distance of the to-be-measured wafer and the upper and lower edge pixel distance of the standard wafer acquired by the imaging module. And the processing module is used for determining the inclined sheet angle according to the first straight slope, the second straight slope and the third straight slope of the upper edge or the lower edge of the wafer obtained by fitting the left area image, the central area image and the right area image obtained by the imaging module respectively. And the processing module is used for determining the warping amount according to the corresponding size of the object space pixel and the maximum longitudinal pixel interval of the upper edge or the lower edge of the wafer obtained by the left region image, the central region image and the right region image obtained by the imaging module, so that the quantitative detection of the state of the wafer to be detected is realized.

In a volumetric display and a head-mounted display having the volumetric display, the volumetric display includes a plurality of self-luminous display panels and a plurality of shutter panels. The display panels are configured to display an image forwardly, and are spaced apart from each other along a forward and backward direction. Each of the shutter panels is disposed between the self-luminous display panels adjacent to each other. A pixel of the self-luminous display panel corresponds to a pixel of the shutter panel. The shutter panel adjusts transmittance of a light emitted at a plurality of the pixels of the self-luminous display panel which is disposed at a rear side of the shutter panel, for each pixel.

The invention relates to the technical field of optical detection, in particular to an LED chip defect detection method and system, and the method comprises the following steps: obtaining an LED chip wiring area pattern layer, positioning interval change pixel point pairs, dividing image blocks, constructing a crack trend through a path consistent with the extension direction, supplementing a wiring boundary extension pattern layer, and obtaining an LED chip wiring area pattern layer. And extracting an electrode tail end brightness sequence, screening out disturbance lines, and concluding the disturbance lines as defect image areas according to a position range. According to the method, the crack trend is screened through the path extension direction, direction line segments are formed through communication, the coherence of structure extension information is enhanced, the wiring boundary is covered by a layer supplementing synchronous extension area, the discrimination precision of the spatial corresponding relation is enhanced, and the boundary brightness detection is combined with mirror image contrast to remove interference. A symmetric disturbance area is reserved as a judgment basis, a path extension trend and a wiring area direction are spatially matched, a crack propagation path identification chain is constructed, and defect area tracking integrity and stability are improved.

The invention provides a bidirectional decoupling translation unsupervised defogging network with feature and pixel comparison representation, decoupling modeling of haze and clear content in an image is realized through a feature and pixel comparison mechanism, and paired clear images do not need to be supervised and trained. The whole network is composed of five key modules: 1) a content recovery branch adopts a multi-scale residual structure to recover image details and textures, and cooperates with a semantic enhancement and color correction module to improve visual quality; 2) the parameter estimation branch estimates a transmissivity map and atmospheric light based on an atmospheric scattering model, and improves the prediction accuracy through context modeling; 3) a feature comparison learning module strengthens the discrimination of clear and fog features; 4) introducing closed-loop consistency by the reverse fogging structure based on reverse physical modeling, and providing an unsupervised training signal; and 5) a pixel comparison mechanism is used for constructing a comparison path by using the direct / indirect defogged image, the reverse fog-added image and the original image, and the network discrimination capability and generalization performance are enhanced. The experiment is carried out on a real natural fog image data set, the result shows that the proposed method is superior to the existing defogging method in the aspects of structural fidelity, detail reduction and color consistency, and the practicability of the method in a complex environment is verified.

Disclosed is a method for enabling exposure-guided three-dimensional (3D) reconstruction, including (i) receiving Low Dynamic Range (LDR) input image captured by camera; (ii) rendering High Dynamic Range (HDR) image corresponding to input image, using HDR 3D reconstruction model pre-trained for HDR image reconstruction of 3D environment; (iii) applying HDR to LDR tone-mapping operator on HDR image, for producing tone-mapped LDR image; (iv) determining first loss function (FLF) between input image and tone-mapped LDR image, FLF comprising weighted pixel value differences between pixels of input image and corresponding pixels of tone-mapped LDR image; (v) determining whether input image has saturated pixel(s), saturated pixel(s) comprises: highlight saturated pixel, shadow saturated pixel; (vi) de-weighting pixel value difference corresponding to saturated pixel in FLF; (vii) back-propagating gradient of FLF with respect to model parameters, through differentiable render function of HDR 3D reconstruction model, for adjusting model parameters in way that reduces FLF.

A method of training a machine learning model to identify image features including providing training image data including pixels, assigning a groundtruth annotation to each pixel relating to a respective pixel and each groundtruth annotation indicating whether or not that the pixel corresponds with an image feature, providing an ignore mask including a set of ignore flags relating to a respective pixel and each ignore flag providing an indication that the pixel should be ignored, for each pixel, receiving a prediction value from the machine learning model indicating a probability of the pixel corresponding with an image feature, for each pixel which has no ignore flag, determining a loss value based on the prediction value and groundtruth annotation for that pixel, and training the machine learning model based on the loss value, and for each pixel having an ignore flag, ignoring the prediction value for that pixel.

A computing system for generating a gain map-based enhanced High Dynamic Resolution (HDR) image receives an HDR image comprising a plurality of pixels, each pixel having one or a plurality of brightness values for each of a plurality of color components, applies at least a first tone mapping algorithm to each pixel in the HDR image, generates a gain map using the HDR image and the SDR image, generates a gain map-based enhanced HDR image based on the SDR image and the gain map, and generates an output based on the gain map-based enhanced HDR image.

The invention belongs to the technical field of remote sensing image processing, computer vision and deep learning, and particularly relates to a remote sensing image erosion gully semantic segmentation method based on improved OfficientNet-UNet. Comprising the following steps of 1, data preparation and data preprocessing; 2, constructing and enhancing a data set; step 3, model construction and strategy training; and 4, performing contrast experiment and result evaluation. According to the method, detail features of ground features can be more accurately captured, an overfitting phenomenon caused by too high model complexity is reduced, so that the classification precision and boundary recognition accuracy of land coverage data are effectively improved, weights of different types of samples can be automatically adjusted in the training process, and the training efficiency is improved. Particularly, the contribution of background pixels to a loss function is reduced, so that the problem of dominant training of the background pixels is effectively relieved; the method has good expansibility.

Provided is an EVS to which an image plane phase difference method AF function can be easily applied.A photodetection device includes: a first pixel configured to photoelectrically convert incident light from a portion on one side to generate a first current; a second pixel configured to photoelectrically convert the incident light from a portion on a side opposite to the portion of the first pixel to generate a second current; a first luminance circuit provided individually corresponding to the first pixel and configured to generate a first luminance signal according to the first current; a second luminance circuit provided individually corresponding to the second pixel and configured to generate a second luminance signal according to the second current; a first current comparison circuit provided individually corresponding to the first pixel, and configured to compare the first luminance signal with a reference signal and convert the first luminance signal into a first digital signal; a second current comparison circuit provided individually corresponding to the second pixel, and configured to compare the second luminance signal with the reference signal and convert the second luminance signal into a second digital signal; and a control unit configured to adjust a focal position of the incident light on the basis of the first digital signal and the second digital signal.