289 results about "Pixel image" patented technology

The embodiment of the invention provides a multi-modal data fused postoperative plastic surgery infection early warning method and system. The method comprises the following steps: firstly, performing quality evaluation and restoration on postoperative wound images, clinical texts, wearable vital signs and baseline features; performing double-flow coding and priori segmentation on the repaired image to obtain a coarse mask; constructing a super-pixel graph, obtaining a topological consistent fine mask through a graph neural network, and quantifying the erythema / exudation area; the text and the vital signs are coded respectively and then fused with the image features and the masks through cross-attention and self-attention, and a fusion implicit vector and uncertainty are obtained through quality weight gating; an individualized dynamic threshold value is constructed by combining baseline risk, logarithmic probability calibration and a sliding window trend, a confidence band is output by using conformal prediction, and early reliable infection early warning is realized.

The invention discloses a building plane element recognition method based on a graph neural network and a convolutional neural network, and belongs to the technical field of building plane element recognition. The identification method comprises the following steps: inputting a building plane pixel image, and carrying out preprocessing through image layer screening and information extraction to obtain a binary pixel image; vectorizing the binarized pixel image to obtain a vectorized image; performing image segmentation on the vectorized image; using the segmented vectorized image to construct a regional adjacency graph; performing regional adjacency graph optimization on the regional adjacency graph through graph analysis and regional merging by using a graph neural network, and identifying a room edge; calculating the area of the room; using the convolutional neural network image recognition model to recognize articles in the room; and performing room function prediction according to an identification result. According to the method, the problem of shape blurring caused by convolution in a traditional method is avoided. The graph neural network model training effect is improved, and the classification accuracy is improved. The method is suitable for various planar graph styles and is high in universality.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention relates to the technical field of deep synthesis, and discloses a deep synthesis service instantaneous algorithm, comprising the following steps: preprocessing input data, including data cleaning, format conversion and feature extraction; inputting the preprocessed data into an improved neural network model for feature learning and synthesis calculation, wherein the improved neural network model is optimized by introducing an attention mechanism and a lightweight network structure; a dynamic computing resource allocation mechanism is adopted, and corresponding computing resources are allocated to the deep synthesis tasks according to real-time computing loads and task priorities; and outputting a synthesis result. Through data preprocessing optimization, lightweight model design and dynamic resource allocation, the average delay of the algorithm for processing a frame of 256 * 256 pixel image is reduced to be within 80ms, the requirement of a real-time interaction scene is met, the delay is reduced by more than 60% compared with an existing algorithm, and the real-time performance can be improved.

The invention provides a method for correcting the layout of a photomask, and is applied to the technical field of semiconductors. According to the method and the device, a plurality of pixel graphs of which the interval lengths do not meet a preset range are screened out based on the interval lengths between adjacent pixel graphs, and then the key sizes of the screened pixel graphs are multiplied by N times according to different proportions, so that a plurality of first test photomasks are constructed; and then determining the optimal magnification ratio of the initial photomask based on the combination of the wafer data, the incremental limit suboptimal solution and the two-side clamping theorem, namely obtaining the target photomask, so that the unexpected effects are that the optimal magnification ratio of each pixel graph in the initial photomask can be rapidly determined only by using a small part of pixel graphs in the initial photomask, and the target photomask is obtained. The manufacturing efficiency and quality of the photomask and the space utilization rate of the photomask and the wafer are improved, and the manufacturing cost of the photomask is reduced.

The invention relates to the technical field of light field image super-resolution reconstruction, in particular to a light field image spatial super-resolution method based on a multi-scale large kernel attention mechanism, which comprises the following steps: inputting a target light field image into a trained spatial super-resolution reconstruction model, and outputting a spatial super-resolution reconstruction image of the target light field image; in the spatial super-resolution reconstruction model, performing feature extraction on an input light field image through a shallow feature extraction network to generate a shallow feature map; respectively extracting angle features, spatial features and epipolar plane image features from the macro-pixel image of the shallow feature map, the sub-aperture image for generating the macro-pixel feature map and the epipolar plane image of the shallow feature map through a large kernel feature extraction network, and fusing to generate a 4D light field feature map; and performing up-sampling reconstruction on the 4D light field feature map through an up-sampling reconstruction network to generate a spatial super-resolution reconstruction image of the input light field image. According to the invention, the reconstruction quality of the spatial super-resolution of the light field image can be improved.

The invention relates to a laser radar point cloud densification method and device fusing image information, and a medium. The method comprises the following steps: fixing a visual camera and a laser radar on a rigid tool, and carrying out joint calibration and data alignment; a laser radar is used to collect three-dimensional point cloud data, and a visual camera is used to shoot a scene; performing super-pixel segmentation on the image by using an image brightness linear iterative clustering algorithm; mapping the three-dimensional point cloud data into a segmented two-dimensional image superpixel pattern spot region by using a jointly calibrated camera model conversion relationship to realize feature clustering of the original three-dimensional point cloud of the laser radar; performing curved surface fitting on a clustered result by using a random sampling consistency algorithm; and linear interpolation is carried out on the fitted curved surface, dense points which are not covered by the original point cloud of the laser radar are supplemented and generated, and densification of the point cloud of the laser radar is realized. According to the method, the point cloud densification precision and practicability are improved, and technical support is provided for automatic driving, robot navigation and three-dimensional reconstruction.

The invention discloses a blade health degree detection method and system based on transfer learning. The blade health degree detection method comprises the following steps: acquiring real-time vibration data of a fan unit blade; and converting the real-time vibration data into a time sequence oscillogram. And converting the real-time vibration data into a two-dimensional pixel image by adopting a Grubrum angle field algorithm. And identifying the time sequence oscillogram according to a pre-constructed first identification model. And identifying the two-dimensional pixel image according to a pre-constructed second identification model. And fusing the classification result of the first identification model and the classification result of the second identification model based on a weighted voting strategy to obtain a final classification result of the health degree of the fan unit blade. According to the method, high-precision, low-cost and real-time detection of the health degree of the fan blade is achieved, and the defects that in the prior art, only basic structure monitoring is concerned, calculation complexity is high, and dependence on data is high are overcome.

The invention relates to the technical field of video image recognition, in particular to an idle parking space detection method based on maximum matching and parking space segmentation, which comprises the following steps of: S1, calibrating all parking spaces in a picture under the view angle of a camera to form a parking space calibration pixel map and a JSON dictionary configuration file; s2, performing semantic segmentation on an input video stream by using a segmentation model, and performing BIFPNEMA bidirectional cross-scale connection and a weighted feature fusion model by using a picture to obtain a detection result graph of a parking space occupancy condition; and associating the parking space attribute of the calibration pixel map to the corresponding parking space of the detection result map through a mapping relationship, and generating a detection result map with the attribute. And S3, filtering out misrecognized parking spaces and irregularly occupied parking spaces by using a multi-priority matching algorithm on the parking space calibration pixel map obtained by calibration and the detection result map to obtain idle parking spaces and occupied parking spaces. S4, the detected parking spaces are output in a serialized mode, and the attribute information and the state information of the parking spaces are combined and output to a service platform.

The invention discloses a deep learning-based DCB substrate internal microcrack water immersion ultrasonic adaptive detection method, which comprises the following steps of: collecting a DCB substrate ultrasonic image generated by water immersion ultrasonic C scanning detection, preprocessing, and constructing a deep learning-based DCB substrate microcrack identification network model; comprising a feature extraction layer, three deep convolution modules introducing an adaptive space attention mechanism, a global average pooling layer and a full connection layer. Training the network model by using DCB substrate ultrasonic detection imaging and classification labels thereof in the training set, adjusting model parameters by using the verification set, and evaluating the performance of the network model by using the test set; and deploying the trained network model into water immersion ultrasonic detection equipment, performing real-time detection on the DCB substrate, generating ultrasonic data, preprocessing the ultrasonic data into a unified pixel image, analyzing the image by using the network model, identifying microcracks and giving out a judgment instruction.

A switchable display system includes a display panel, an image source, a switching signal source, and a processing circuit. The display panel includes pixels containing a first pixel. The image source is used to provide image data. The switching signal source is used to provide a switching signal. The processing circuit is coupled to the display panel, an image source, and a switching signal source, stores a correction algorithm, and is used to drive the display panel to display a three-dimensional or two-dimensional image according to the switching signal. When the processing circuit drives the display panel to display the two-dimensional image according to the switching signal, the processing circuit switches a first brightness of the first pixel to a second brightness, which is determined based on brightness values in the image data of pixels of same color adjacent to the first pixel and weight values of the correction algorithm.

The invention belongs to the technical field of image processing, and particularly relates to a pet behavior analysis method based on image recognition, and the method comprises the steps: 1, obtaining a video frame sequence, carrying out the superpixel segmentation of each frame of image in the video frame sequence, and obtaining a superpixel image composed of a plurality of superpixel blocks; step 2, constructing a Poisson multi-Bernoulli hybrid tracking link by taking the region association graph as input, and segmenting a trajectory entity set into a time sequence fragment set; 3, constructing a skeleton * object * scene hypergraph by taking the time sequence fragment set as input; and 4, finally outputting a behavior analysis result carrying an object participation type and a scene type according to a feeding action segment and a water drinking action segment in the strong recognition initial result of the interactive behavior. According to the invention, unified modeling and interpretable behavior identification of pets, objects and scenes are realized, and the method has the advantages of high robustness, high precision and real-time performance.

The invention provides a nondestructive testing method for the solidification state of a solid-state battery based on multi-mode ultrasound, and the method comprises the steps: carrying out the ultrasonic surface scanning of the solid-state battery through employing an ultrasonic testing device, and collecting a transmission signal and an echo signal of an ultrasonic wave; calculating the transmission signal sound velocity and the transmission signal peak-to-peak value of each point on the scanning surface of the solid-state battery; mapping to generate an HSV (hue, saturation and value) color pixel map; judging the curing state of the solid-state battery according to the transmission signal sound velocity and the HSV color pixel map; when the curing state is not good, positioning a suspicious defect area according to the HSV color pixel map; the curing defect type is determined from the display of the HSV color pixel map and a B-scan map generated from the echo signal. According to the method, nondestructive detection of the solid-state battery curing state can be realized, and cooperative improvement of detection efficiency and accuracy can be realized.

The invention discloses a wetland human interference activity rapid identification method based on remote sensing and deep learning, and relates to the technical field of image identification, and the method comprises the steps: 1, carrying out the radiometric calibration, geometric registration and pixel-level alignment of a multispectral remote sensing image of a target wetland obtained at different time phases, and dividing the multispectral remote sensing image into image tile units according to a preset size; 2, two image tile units of different time phases at the same geographic position are paired to form a tile pair, and after fusion of a spectral coding branch and a time coding branch, a change coding graph is output and used for representing wetland surface change characteristics; step 3, constructing a double-layer adjacent pixel graph by taking the change coding graph as input, and performing segmentation under multiple scales through a progressive form graph segmentation algorithm to obtain communicated change clusters; and 4, comparing the attribute values of the change clusters with a template threshold value, and carrying out type interference activity type judgment on each change cluster. The method has rapid processing capability and good adaptability.

The invention discloses an optical fiber single-pixel imaging method and device based on sparse optimization and neural network fusion, and the method comprises the steps: constructing a collimation illumination system, carrying out the spatial modulation of incident light, introducing a collimation illumination mechanism, and obtaining a spatial speckle illumination light field of a multimode optical fiber; performing sparsity extraction analysis on the spatial speckle illumination light field by using a sparse optimization algorithm, removing a high redundancy mode, and selecting a speckle illumination mode with the lowest cross correlation; a speckle illumination mode after sparse optimization is used as input, a neural network model constructed based on a soft threshold iterative algorithm is trained, and an optical fiber single-pixel image reconstruction model is established; performing sparse optimization speckle illumination on a target object, acquiring corresponding total light intensity response data, inputting the data into the trained optical fiber single-pixel image reconstruction model, realizing image reconstruction and outputting a final target image; the method has anti-defocusing capability and anti-disturbance robustness, and can still keep good optical fiber single-pixel image reconstruction performance under the condition of low sampling rate.

The invention relates to the technical field of refrigeration type infrared focal plane detector imaging, in particular to a data transmission method of a refrigeration type infrared focal plane detector imaging system, which comprises the following steps of: receiving LVDS (Low Voltage Differential Signaling) data acquired by a refrigeration type infrared focal plane detector in an imaging system consisting of a PC (Personal Computer) upper computer, a power supply, a main control board and the refrigeration type infrared focal plane detector; the method comprises the following steps: performing camera link conversion, transmitting a converted differential signal to an upper computer through a video cable for imaging, performing 2 * 2 pixel image array transmission on LVDS (Low Voltage Differential Signaling) data by utilizing an FPGA (Field Programmable Gate Array), representing the position of each pixel in the 2 * 2 pixel image array by utilizing four different identifiers, digitalizing each pixel by adopting 19-bit data, and transmitting the digitalized data to the lower computer. 19-bit digital data of one pixel is transmitted in a parallel transmission mode. According to the invention, the 2 * 2 pixel image array is adopted for data transmission, so that the algorithm operation of the pixel data is realized, and the image processing is facilitated; four-channel serial LVDS differential signals are adopted to transmit data, and external electromagnetic interference can be effectively offset.

The invention provides a pathological image analysis method and device based on multi-instance learning, equipment and a storage medium, and the method comprises the steps: receiving a lung pathological section image, segmenting the lung pathological section image into a plurality of 256 * 256 pixel image blocks, and screening and reserving effective image blocks containing a tissue structure; extracting a high-dimensional feature vector of each effective image block by using a self-supervised pre-trained deep neural network on the large-scale dyeing pathological image; the features are input into a multi-branch attention module containing 15 parallel branches, self-attention, cross-attention and global attention mechanisms are executed respectively, and local spatial dependence, cross-image block similarity and global relevance are modeled; and carrying out weighted aggregation on the basis of the processed attention weight to generate a global feature of the slice image, outputting a pathological category through a classification network, and supporting heat map visualization and feature clustering analysis. According to the method, unlabeled region information is effectively utilized, and the pathological classification precision and the model generalization ability are improved.

The invention belongs to the technical field of intelligent traffic, particularly relates to a tunnel global tracking method based on an old camera re-identification technology, and aims to accurately realize global tracking and trajectory generation of a tunnel traffic target in a low-cost manner. The method comprises the steps of receiving an RSTP data stream, processing the RSTP data stream through a detection algorithm to obtain a sensing result, and feeding back the sensing result to the image tracking module. And processing a time sequence sensing result, adding vehicle speed information and a vehicle ID, and generating a tracking result including the vehicle ID, a target frame, a vehicle speed, a vehicle type, a color, a timestamp, a base station number and a camera number. On the basis of vehicle pixel picture data, upstream sample library candidates are matched for a current point position sample library target, if matching succeeds, the target inherits the global ID, if matching fails, the point position ID is reserved, and the target is output to a downstream point position and a simulation platform after the sample library is updated. And the trajectory generation post-processing module constructs a target matching relationship between adjacent camera sections through a frame insertion mode according to a feature re-identification result, and generates a tunnel global complete trajectory chain.

Image sensors, imaging systems, and methods for constructing image sensors. The image sensor includes a pixel array. The pixel array includes a first photosensitive region, a second photosensitive region, a spectral router, and a spectral filter. The first photosensitive region is configured to detect visible light within a color wavelength range. The second photosensitive region includes a plurality of light scattering structures. The second photosensitive region is configured to detect infrared light. The spectral router is positioned over at least the first photosensitive region. The spectral router is configured to route the visible light within the color wavelength range to the first photosensitive region. The spectral router is also configured to route the infrared light to the second photosensitive region. The spectral filter is positioned over the spectral router. The spectral filter is configured to block visible light outside of the color wavelength range.

The invention provides a multi-nozzle jet flow uniformity determination method based on vision, and relates to the technical field of thick plate hot rolling and computer vision. According to the method, key frame extraction and preprocessing are carried out on collected video data; jet region identification is carried out by using an image segmentation technology, and density reference values are calculated pixel by pixel for the identified region; designing an index for measuring the jet flow uniformity according to a density reference value and supporting thermodynamic diagram visualization and analysis; through a mode of combining visual image processing and linear calibration, efficient and accurate quantitative evaluation on the jet flow uniformity of the multiple nozzles is realized. Moreover, the method can be realized through laser jet visualization software developed on the basis of a Python language, necessary array acceleration operation is carried out in combination with a Cupy library, the calculation speed of a single 1080 * 720-pixel image is smaller than 800 ms, and the requirements of actual industrial deployment and real-time detection are met.

The video stream management system (150) includes a video controller (152) that renders video in real-time. Further, the video stream management system (150) includes a display (102) that is communicatively coupled to the video controller (152) and displays a primary video feed (168) that includes the real-time rendered video. The video controller (152), the display (102), or a combination thereof, embeds a pixel pattern (164) in the primary video feed (168). Additionally, the video feed management system (152) monitors one or more displayed images on the display (102) to identify an error in the primary video feed (168).

The invention provides a coarse and fine granularity progressive front-end code generation system and method based on a diffusion model and knowledge distillation. The system comprises an interface skeleton diffusion generation module, an interface skeleton code distillation module and a front-end code progressive fusion module. The interface skeleton diffusion generation module is used for generating a conditional discrete diffusion model facing a UI skeleton, understanding the layout of UI components in an input pixel image and completing the extraction of the layout relationship of the components; the interface skeleton code distillation module is used for completing lightweight implementation of layout code generation by learning a layout structure and a layout code relationship marked by a teacher model based on a knowledge distillation technology; and the front-end code progressive fusion module is used for establishing a relation among pixel map coordinates, a layout structure and a code document object model by utilizing a divide-and-conquer idea, and realizing final code fusion through component coordinates and layout code indexes. According to the method, the front-end low-code generation efficiency and quality are improved.

This invention discloses a text image restoration method based on generative adversarial networks (GANs), comprising the following steps: S1: Collecting images containing inscriptions and random occluded images, performing scale normalization to generate k×k pixel images, and establishing an inscription image database, where k is a positive integer; S2: Constructing a generative adversarial network (GAN); S3: Using images from the inscription image database as training samples, training the GAN and optimizing the parameters of the generator and discriminator in the GAN; S4: Replacing the pixel values ​​of the occluded or damaged areas in the text image to be restored with the pixel values ​​of the corresponding areas in the generated text image. This invention effectively simulates the structural features of text images, improves the accuracy and quality of text image restoration results, and effectively restores damaged or contaminated text images, providing a new method and approach for image restoration.

The utility model discloses a light beam offset device, an image resolution improving module and a near-to-eye display device, the light beam offset device comprises a light beam offset element and a driving device, the driving device is used for driving the light beam offset element to deflect N times in each period, and enabling the light beam offset element to sequentially go through an initial state and N-1 deflection states in each period, so that T low-pixel sub-images displayed by the image display device pass through the light beam offset element and the projection lens and then correspondingly output one non-offset sub-image and N-1 offset sub-images to human eyes in sequence, therefore, human eyes can feel a high-pixel image obtained by fusing and superposing one non-shifted sub-image and N-1 shifted sub-images; wherein N and T are integers, N > = 2, and 1 < = T < = N. According to the utility model, small volume can be realized and high-definition pictures can be provided.

Disclosed is a display apparatus including: a substrate including a plurality of pixels having a plurality of sub-pixels; a pattern portion disposed on the substrate and concavely formed in a non-light emission area between the plurality of the sub-pixels; and a reflective portion disposed on the pattern portion, wherein each of the plurality of subpixels includes a pixel electrode disposed to be spaced apart from the reflective portion, and the pixel electrode includes a main body and a plurality of protrusions protruding from the main body toward the reflective portion. This configuration enhances the reflective properties in the non-light emission regions, helping to improve overall display efficiency, brightness uniformity, and image clarity.

To provide a technique advantageous for achieving both a frame rate and distance measurement accuracy.SOLUTION: A distance image creation device comprises a plurality of pixels, and an image creation unit for creating a distance image in each of a plurality of distance measurement frame periods. In each distance measurement frame period, a plurality of sub-frames are acquired by a plurality of detection operations different from each other in the time from light emission timing in a light source until an exposure period in which each pixel detects light. The distance image creation device further comprises an event detection unit that, for each of the plurality of pixels, detects a change in a signal value between at least two distance measurement frame periods. In each distance measurement frame period, for the pixel for which the event detection unit detects the change, the image creation unit acquires the pixel value of the distance image on the basis of the plurality of sub-frames. For the pixel for which the event detection unit does not detect the change, the image creation unit does not acquire the pixel value of the distance image, or when a set condition is satisfied, acquires the pixel value of the distance image on the basis of the plurality of sub-frames.SELECTED DRAWING: Figure 14

The invention provides a method for processing a curve in an image, and the method comprises the steps: obtaining the scale lines of a plurality of coordinate axes on a coordinate image and a pixel image of the coordinate image, and enabling the coordinate image to comprise a coordinate grid and a function curve; determining a pixel grid line corresponding to the coordinate grid and a pixel curve of the function curve in combination with the pixel map; determining a target axis pixel coordinate of each scale line on each coordinate axis according to the axis pixel coordinate of the pixel grid line on each coordinate axis; according to a scale value of an adjacent scale line corresponding to a first numerical value of a target variable and a target axis pixel coordinate, determining a pixel point corresponding to the first numerical value on the pixel curve; and determining a second value of the preset variable corresponding to the first value of the target variable on the function curve according to the scale value of the corresponding adjacent scale line of the pixel point on the coordinate axis of the preset variable and the pixel coordinate of the target axis.

The application relates to a dual-branch hyperspectral image classification method based on a graph convolutional neural network and an attention mechanism, which comprises the following steps: inputting a hyperspectral original image to data for pretreatment; inputting the data after dimension reduction into a CNN attention mechanism branch, and obtaining processed branch features through an attention mechanism module, a data processing module, an attention mechanism module and convolution operation; inputting the data after dimension reduction into a GCN branch, performing simple linear iterative clustering, segmenting into k superpixels, encoding the k superpixels, obtaining superpixel graph nodes, inputting the graph nodes into a GCN module for feature extraction, decoding the extracted superpixel node features into pixel-level branch features; performing feature fusion on the two branches, using a cross-entropy function as a loss function to train the model, and using a softmax function to obtain a class label probability. The application takes limited hyperspectral images as research objects, improves high classification precision, and ensures classification speed.

The application discloses a strip steel coiling time side guide plate spark detection method based on image recognition, which comprises the following steps: 1, an image collector (8) is arranged above the side guide plate, the image collector faces the strip steel (2) and the side guide plate, and is connected with a control system through a controller (7); 2, the image collector sends images to the controller, the controller converts the images into pixel images, and initializes a time domain median background model; 3, whether the image serial number is continuous is determined, if yes, step 4 is executed, and if not, the step 1 is returned; 4, a spark connected domain is extracted, and the actual width of the spark is calculated according to the spark connected domain; 5, whether the time domain median background model is updated is determined, if yes, step 6 is executed, and if not, step 7 is executed; 6, the time domain median background model is updated; 7, a spark recognition result is output; 8, whether the spark detection program is exited is determined, if yes, the detection is ended, and if not, the step 1 is returned. The application can recognize the spark condition between the side guide plate and the strip steel through image collection.