502 results about "Image layer" patented technology

In order to solve the problem that the bottleneck exists when image-text features are fused in a traditional anti-modal recognition model and deep-level interaction information between modals cannot be fully mined in an existing method, the invention provides a multi-modal anti-modal recognition model (MSCAG) fusing two-way cross attention and a gating mechanism, and a technical scheme is provided for solving the problem that the traditional anti-modal recognition model has the bottleneck when the image-text features are fused in the traditional anti-modal recognition model. Related attention features on a text layer and an image layer are respectively refined through a bidirectional cross attention mechanism, the text attention features, the images and basic features of the texts are integrated through residual connection, and the image attention features, the texts and the basic features of the images are integrated through residual connection; a gating mechanism is applied to enhance information interaction and key area focusing between the two modes. And the context features and the local features are combined to realize more reliable chiffon recognition. The invention provides an innovative method for multi-mode siphonage recognition, has important practical application value, and can be applied to siphonage recognition of netizen comments in social media.

The invention relates to the technical field of computer vision image enhancement, and provides a zero sample learning defogging image enhancement method and device based on image decomposition, and the method comprises the steps: introducing three initial image layer generation networks: an initial clean image layer generation network, an initial atmospheric light image layer generation network, and an initial transmissivity image layer generation network; a target atmospheric light layer generation network and a target transmissivity layer generation network are obtained through joint training and are respectively used for determining a target atmospheric light layer and a target transmissivity layer of a to-be-defogged image, and a defogged image corresponding to the to-be-defogged image is solved by combining an atmospheric scattering model. In the joint training process of the three initial layer generation networks, only the to-be-defogged image is needed, a large number of training samples do not need to be introduced, the problem that in the prior art, the training samples of an image defogging model are difficult to obtain can be avoided, the calculated amount in the training process can be reduced, the training duration of the network can be shortened, and the training efficiency of the image defogging model is improved. And the training time cost of the network is reduced.

The invention relates to the technical field of image processing, in particular to an image segmentation system for medical diagnosis, which comprises a regional characteristic analysis module, a modal selection optimization module, an image local enhancement module, an edge characteristic analysis module and a boundary optimization adjustment module. According to the medical image segmentation method, the gray level distribution, the texture density and the structure contour of the tissue structure in the image layer are analyzed, the system is allowed to accurately measure the characteristic deviation between different modes, the accuracy of medical image segmentation is improved, especially on the processing of the complex tissue structure, the adjacent tissues with different properties can be better analyzed and distinguished, and the medical image segmentation accuracy is improved. Real-time evaluation and adjustment of local contrast enable details of the image to be clearer, image quality is improved, accuracy of edge recognition is improved, by analyzing edge curvature and morphological characteristics, the system can optimize edge trend and reconstruct a boundary path, overall performance of image segmentation is further improved, and image segmentation efficiency is improved. Therefore, the treatment and prognosis effects of the patient are ensured.

The invention relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision. The method comprises the following steps: analyzing acquired to-be-detected wallpaper image data to obtain regional texture variance, edge density and brightness gradient indexes, and generating a scale image layer set; extracting salient edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, and generating a texture feature map set according to the edge anchor point set; processing the texture feature map set to generate a texture significance distribution map; based on the texture significance distribution diagram, main direction distribution is extracted, a direction residual error model is constructed, and a residual error diagram and a direction difference distribution diagram are generated; and establishing a region scoring matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set on the basis of the region scoring matrix. The wallpaper defect detection precision can be improved.

The invention relates to the field of image processing, in particular to a multi-modal guided progressive image generation method, which comprises the following steps of: generating multi-modal features for received data; generating a final layer layout for each main object by using a layout denoising network through a noise sampling and multi-modal feature fusion method; extracting potential codes of a reference image through an inversion algorithm, combining layer text codes, performing iteration denoising through a stable diffusion model, and extracting a reference key value matrix; the initial noise, the final layer layout, the multi-modal features and the reference key value matrix are input into a layered diffusion model, and after multi-step denoising, a main body transparent image layer is generated through decoding; and after all the main body transparent layers are superposed to form a mixed image, driving the image recovery network to generate a target image based on global text coding. According to the method, the defects of an existing method in processing a complex scene are overcome; and the generated image is ensured to be consistent with the text description globally and locally.

The invention provides a three-dimensional super-resolution method suitable for a CT image sequence, and relates to the technical field of medical images. According to the method, image high-frequency information is extracted more accurately through gradual combination of multi-scale deep convolution and an attention mechanism, a medical CT image interlayer super-resolution network which is iteratively refined from coarse to fine is designed, and a cross section slice of a high-resolution middle frame is synthesized by using two continuous slices at spatial positions. The method comprises the following steps of: generating a multi-scale context feature by using a feature pyramid structure, gradually fusing a two-way deformation estimation graph through an IIR (Infinite Impulse Response) module by combining a global two-way distortion function (GBDF) with two-way optical flow information and the multi-scale context feature, complementing detail information for an intermediate frame by combining an STIFOF (Spatial-Temporal Interval-Frame Optimization Function) function to realize motion compensation, and finally obtaining a high-resolution intermediate frame prediction graph.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors assign an identifier that specifies a number of resources and a category associated with a respective image layer of a plurality of image layers. One or more processors, in response to receiving a user request, identify image layers of the plurality of image layers that match the identifier based on dependencies between the plurality of image layers. One or more processors can retrieve matched layers based on the functionality of respective image layers and the dependencies of those respective image layers.

A method is provided for unsupervised 3D modeling and geometric measurement of additively manufactured parts. The method includes obtaining near-infrared (NIR) images for a welding process for a welded part. The welded part includes welded metal and agglomerated powder particles. The method also includes generating a multi-dimensional dataset based on the NIR images, including cropping the multi-dimensional dataset to a region of interest. The method also includes detecting melted regions in image layers of the multi-dimensional dataset to obtain an output volume. The method also includes detecting agglomerated powder in the output volume to obtain melt masks. Each melt mask indicates weld pixels for a respective image layer. The method also includes applying a multi-layer predictive model to account for multi-layer weld penetration, based on the melt masks, to obtain an output data mask that represents a 3D model and geometric measurements for the welded part.

The invention provides a free breathing heart fat fraction and multi-relaxation parameter simultaneous quantitative magnetic resonance imaging method, a free breathing heart fat fraction and multi-relaxation parameter simultaneous quantitative magnetic resonance imaging device, a terminal and a medium. The method comprises the following steps: scanning a plurality of imaging layers on heart tissues of a detected object in a free breathing state to obtain a double-echo image of each cardiac cycle corresponding to each imaging layer and an electrocardio trigger signal; simulating an evolution process of a magnetization vector based on a Bloch equation and an electrocardio trigger signal, and establishing a dictionary; on the basis of the double-echo image after image processing, water-fat separation operation based on mixed multi-echo is executed, and a water signal image and a fat fraction graph are obtained; and obtaining a relaxation parameter group diagram of the detected object in a mode of matching the water signal image with the dictionary. Collection is carried out when the detected object freely breathes, the burden of the detected object is relieved, and the fat fraction quantification precision and the water-fat separation accuracy are guaranteed.

The invention provides an image restoration and generation method based on a conditional generative adversarial network, and the method comprises the steps: a camera APP obtains contour key point data of a user figure proportion through carrying out the high-resolution feature extraction of an input image, carries out the three-dimensional reconstruction of a waist line and a shoulder breadth size, and obtains a contour key point data of a user figure proportion; generating an initial feature mapping graph containing wrinkle density distribution and local deformation amplitude; according to the initial feature mapping graph, adopting a region segmentation technology to separate a body contour from a clothing region, extracting a texture stretching degree and contact point stress distribution, determining a distribution condition of clothing and body contact points, and generating a segmentation result for dynamic adjustment; and for the to-be-optimized detail area list, performing enhancement processing on the texture stretching degree of the contact points of the clothes and the body and the stress distribution of the contact points, obtaining wrinkle layer depth data of a local area, and generating an optimized image layer.

The invention discloses an image denoising method and system based on a differential equation and a deep learning network. The method comprises the following steps: inputting a noise image subjected to image preprocessing into a differential equation NODE model and an improved deep learning network UNet; extracting multi-level features of the noise image layer by layer through a multi-scale channel feature fusion module of an improved UNet network; attention is increased for shallow information in the network through an attention mechanism module; and carrying out continuous time dynamic modeling by using a NODE model, taking the improved UNet network as a neural network vector field in the NODE model, and simulating a continuous time evolution process through an ODE solver to obtain a clear image. Through the fusion of ODE modeling and the deep learning network, the dependence on large-scale annotation data is reduced, parameters are evolved and optimized by using continuous time characteristics, the generalization ability of the model to complex noise is improved, and a good denoising effect can be obtained under the condition of few sample training.

The invention provides an unmanned aerial vehicle image target detection method and device based on dynamic deformable convolution feature fusion. The method comprises the steps of inputting a to-be-detected unmanned aerial vehicle image for preprocessing; extracting multi-scale features from the input preprocessed image layer by layer to obtain an input feature map; inputting the input feature map into a DDCM-based multi-level fusion feature fusion network for feature fusion to obtain multi-level fusion features, and extracting target deformation features in each level structure of a multi-level fusion feature pyramid structure by using dynamic deformable convolution through a dynamic deformable convolution feature learning module, according to the dynamic deformable convolution, a global coordinate attention mechanism is adopted to fuse position information and global features, and a dynamic space offset and a modulation mask are generated; and inputting the multi-level fusion features into a multi-scale perception detection head for target detection. According to the invention, small target leak detection and deformation target positioning deviation can be reduced, and the detection precision and robustness are improved at the same time.

The invention provides a grid clustering method and device, equipment and a storage medium, and the method comprises the steps: carrying out the layer-by-layer grid division of a target region represented by a target image according to each resolution corresponding to the target image, and obtaining N target grids corresponding to the target region; counting the grid density corresponding to each target grid to obtain N grid densities; sorting the N target grids according to the sequence of the corresponding grid densities from high to low to form a grid sequence; and clustering the N target grids according to the sequence of the N target grids in the grid sequence and the grid density corresponding to each target grid to obtain M clusters. In the embodiment of the invention, the target area is subjected to grid division through the hexagonal grid, so that the deviation and error of the clustering result can be reduced; in this way, the curvature and the non-rectangular terrain of the target area are adapted through a hexagonal grid clustering method, and the accuracy of a clustering result is improved.

The embodiment of the invention discloses a digital intelligent risk detection method and equipment for a high-conversion perovskite battery, and a medium, belongs to the technical field of battery detection, and solves the problems of energy waste and production interruption caused by maintenance after the high-conversion perovskite battery breaks down. Carrying out batch fusion on multi-scale image layers corresponding to the high-conversion perovskite battery image, and carrying out temperature labeling on the fused image through a temperature mapping algorithm to generate a defect thermodynamic diagram; performing feature vector extraction on the defect thermodynamic diagram to determine a defect grade corresponding to the high-conversion perovskite battery based on a feature vector; under the condition that the defect grade is greater than a preset grade threshold value, converting defect area information in the high-conversion perovskite battery into a control signal, and adjusting a shooting light source and a shooting angle so as to determine new defect area information; and performing path evolution on battery defects through a digital twinborn model, and obtaining risk prediction information of the high-conversion perovskite battery based on an evolution result.

The invention relates to the technical field of image processing and display, and provides an image processing method based on a vertical synchronization signal, electronic equipment and a storage medium, and the method comprises the steps that the electronic equipment responds to a first vertical synchronization signal, draws and renders, and buffers a first image layer in a first buffer queue at a first moment; and under the condition that the first moment is after the second vertical synchronization signal and the third vertical synchronization signal does not have the first image frame to be refreshed and displayed, the electronic equipment immediately triggers the first image layer at the first moment to carry out image layer synthesis to obtain a second image frame. Wherein the second vertical synchronization signal is after the first vertical synchronization signal and is spaced by a first preset period, and the third vertical synchronization signal is after the second vertical synchronization signal and is spaced by a second preset period. Therefore, when the drawing of the first image layer is delayed and the second image frame can be refreshed and displayed by the corresponding third vertical synchronizing signal, the immediate synthesis is triggered, so that frame loss can be avoided, and the smoothness of image display is ensured.

Techniques according to the present disclosure may include receiving, at a computer, a command for performing an action on a first image layer of a first container repository. A second container repository is determined which has a dependency on the first container repository, based on a dependency graph storing dependencies of a plurality of container repositories. The action is performed on the first image layer of the first container repository and a corresponding action on a second image layer of the second container repository based on the command.

The invention provides an image layering and semantic segmentation method based on prior information. The method comprises the following steps: step 1, acquiring an X-ray image under a security inspection machine as a data set; step 2, rectangular frame labeling is carried out on contraband articles in the pictures in the data set, and clear boundary information is provided for each object; step 3, selecting a BoxI nst model; 4, training the selected BoxI nst model by using the marked data set, so that the BoxI nst model can learn how to generate an accurate mask from a rectangular frame; through training, the BoxI nst model can effectively identify and segment contraband articles in the X-ray image. And 5, optimizing and evaluating the model. The small object detection capability is remarkably improved, the positioning precision is improved, the processing speed is increased, and the overlapped object processing capability is enhanced, so that the defects of detecting contraband articles in a complex scene in the prior art are overcome, and a more efficient and accurate solution is provided for real-time safety detection.

A video coding mechanism is disclosed. The mechanism includes encoding a bitstream comprising one or more layers of coded pictures. A video parameter set (VPS) is also encoded into the bitstream. The VPS includes an output layer set (OLS) mode identification code (ols_mode_idc) specifying that a total number of OLSs specified by the VPS is equal to a number of layers specified by the VPS. The bitstream is stored for communication toward a decoder.

The invention provides a culture symbol transmission method and system based on Sichuan opera facial makeup art patterns, and particularly relates to the field of facial makeup digital transmission. According to the method, multi-level modeling of a Sichuan opera facial makeup pattern from a pixel layer to a component layer and then to a semantic layer is realized by fusing image processing, component contour matching, image structure modeling and an image neural network technology; according to the method, main and auxiliary color block areas and component information in an image can be automatically identified and structurally stored, a spatial semantic relationship of the image is learned through a graph neural network, a structural semantic code is output, and finally a multi-dimensional cultural label index capable of being filed, clustered and classified is generated; the method effectively overcomes the limitation that in the prior art, only stays at an image level and is lack of structure and semantic modeling, enables the Sichuan opera facial makeup pattern to have machine-readable component structure representation and semantic feature representation, and lays a foundation for cultural inheritance and intelligent propagation.

Container image optimization is provided for a deployment environment. An indication of a plurality of container images to be deployed in a deployment to provide a plurality of services within a computing environment is obtained. The plurality of container images of the deployment has a total layer size of a plurality of image layers of the plurality of container images. At least one container image of the plurality of container images is reconfigured to adjust the total layer size for the deployment. The reconfiguring is based on one or more deployment characteristics of the deployment and includes replacing at least one image layer of a container image of the at least one container image with one or more other image layers configured based on the plurality of container images to be deployed.

Disclosed herein are methods and system of implementing an AI-enabled high-fidelity occupancy network, allowing for the prediction of signed distances of voxelized objects in a 3D space surrounding an ego, thereby facilitating enhanced object shape refinement. Through the utilization of camera feeds only, an AI model accurately calculates signed distance values for various voxels in the 3D space. The predicted values can also be rendered by translating signed distance field values into image layers and subsequently stacking the layers to form a 3D representation of the space surrounding the ego. Furthermore, the system can predict and display various ground markings, expanding its functionality beyond traditional lane detection.

A system and method of a state machine mechanism for targeted container creation. The method includes maintaining, in a database, a pool of available image layers and a plurality of compatibility profiles, each compatibility profile of the plurality of compatibility profiles indicates whether a respective available image layer of the pool of available image layers is compatible with other available image layers of the pool of available image layers. The method includes receiving, from a client device, a request to modify a container image including a base layer and one or more image layers. The method includes generating, based on the pool of available image layers and the plurality of compatibility profiles, a state machine for evaluating image layer compatibility for the container image. The method includes generating, by a processing device based on the state machine, a container image file that defines a modified version of the container image.

The invention relates to the technical field of image recognition, in particular to a urinary system CT image tumor benign and malignant identification method based on deep learning, which comprises the following steps: acquiring a urinary system CT image, constructing a map band sequence and extracting gray level distribution, identifying a heterogeneous edge and a texture mutation region, aggregating perturbation map blocks to form an abnormal structure, and identifying the benign and malignant tumors. And fusing multiple types of image layers to complete label integration, and generating a feature recognition image layer. According to the method, the extension recognition capability of the tumor edge external expansion region is enhanced by combining a graph band gray scale aggregation and sequence construction mode, the judgment precision of local heterogeneous change is improved by fusing gray scale kurtosis and migration analysis, and the texture disturbance trend is extracted based on direction vector included angle change. The block gray level fluctuation and gradient relationship supports abnormal structure aggregation identification, spatial coincidence and boundary difference combined screening realizes multi-feature region unified coverage, abnormal region expression definition and structure positioning accuracy are enhanced, and image layer consistency and identification stability are improved.

The invention provides an optical anti-counterfeiting film with a three-dimensional dynamic display effect, the minimum function structure of the optical anti-counterfeiting film comprises a first Moire image layer, a transparent optical refraction layer and a second Moire image layer which generates a Moire effect with the first Moire image layer, one of two coherent Moire images is processed on the front surface of the transparent optical film, and the other of the two coherent Moire images is processed on the rear surface of the transparent optical refraction layer. And the other one of the two coherent Moire images is processed at the corresponding position of the rear surface, and the period of the Moire image has a strict mathematical relationship with the refractive index n of the optical transparent material and the thickness h of the optical transparent material, so that a three-dimensional dynamic pattern effect capable of being observed by naked eyes is realized. According to different processing methods, the manufacturing method of the optical anti-counterfeiting film and the product of the optical anti-counterfeiting film can be realized from the cost of common printing to the cost of high-precision micro-nano processing such as electron beam lithography, the market requirements of various grades can be met, and the anti-counterfeiting difficulty grade can be changed according to different image design and processing methods.

The invention discloses an intelligent screenshot method, device and equipment for a digital pathological section and a storage medium, and relates to the technical field of medical data processing, the method is applied to front-end equipment, and the method comprises the steps that in response to a section screenshot instruction, viewport parameters of a target section image at a current viewport and size parameters of a screenshot visual area are captured; according to the viewport parameters, drawing the slice image layer locked at the current viewport, the first annotation rendering layer and the second annotation rendering layer to an off-screen synthetic canvas corresponding to the size parameters in sequence, and generating initial screenshot data; and according to the viewport parameter and the size parameter, superposing the scale data on the initial screenshot data to generate target screenshot data. According to the method, the section image, the multi-source labeling information and the dynamic scale can be automatically synthesized during digital pathological section screenshot, manual splicing is not needed, errors are reduced, pure front-end real-time screenshot is achieved, rear-end participation is not needed, the response speed is high, and the real-time interaction requirement is met.

The invention discloses an ORB feature extraction method based on an adaptive sampling pyramid in a dynamic environment. The method comprises the following steps: acquiring and preprocessing an original image; identifying key points in the preprocessed image by using a FAST detector, calculating gradient intensity, and generating a pixel point importance score value; dividing the preprocessed image into a plurality of small blocks, and performing dynamic resolution distribution based on the importance score value of each small block; creating a highest-resolution image layer, and performing non-uniform down-sampling on the previous layer according to the resolution hierarchy of each small block to generate an adaptive sampling pyramid; carrying out smooth transition processing on pixel values between adjacent small blocks by adopting a fusion algorithm; the scale space continuity is maintained; gradual change between adjacent layers is controlled; and outputting the multi-scale feature points and the descriptors. According to the method, the ASP algorithm and the ORB algorithm are combined, so that the technical problem of low accuracy and stability of feature extraction of a dynamic environment by a conventional pyramid ORB feature extraction method based on a fixed-scale image is solved.

The invention provides an airborne high-speed image hierarchical compression and encrypted storage method and system, electronic equipment and a storage medium, and relates to the technical field of airborne image processing and data security storage. The method comprises the following steps: acquiring an airborne high-speed image sequence, synchronously monitoring surface pneumatic pressure pulsation data of an aircraft, converting the surface pneumatic pressure pulsation data into a deformable mirror surface group curvature control signal, adjusting mirror surface deformation parameters to compensate optical path wavefront distortion, and generating a stable image; air vortex characteristics are extracted to calculate an intensity distribution diagram, turbulence levels are divided, and corresponding compression strategies are adopted; extracting vortex phase features of each level to generate a dynamic key, and encrypting the layered compressed data stream; according to the invention, layered high-efficiency compression and dynamic encryption storage of airborne high-speed images in a turbulent environment can be realized, and data processing effectiveness and storage security are guaranteed.

Systems and methods for generating container images are described. An image tag comprising a set of code names is received at a virtual image server, wherein the virtual image server comprises a mapping of each code name of the set of code names to a corresponding image file. For each of the set of code names, a corresponding image file is retrieved from an image registry and an image manifest is generated by the virtual image server using the corresponding image file of each of the set of code names. The image manifest may reference the corresponding image file of each of the set of code names as a separate image layer. The virtual image server may provide the image manifest to a container host, which may build a container image based on the image manifest.

The invention discloses a resource usage amount statistical method and system oriented to an application mirror image, and belongs to the technical field of cloud computing and containerization. The method comprises the steps that duplicate removal and calculation are conducted on files of all mirror image layers according to the identification result of the files, and the file storage amount is obtained; collecting resource use state data of the application mirror image and inputting the data into the resource competition game model, so that the resource competition game model outputs the calling weight of each process in each mirror image layer and determines a resource occupation ratio by solving a Nash equilibrium strategy; dividing the current system resource usage data to each current process based on the corresponding relationship between each current process and each current mirror image layer, and determining the resource usage amount of each current mirror image layer according to each resource occupation proportion; and summarizing the resource usage amount and the file storage amount of each layer to obtain the total resource usage amount of the current application mirror image, so that accurate statistics of the resource usage amount can be realized.

The invention relates to the technical field of image processing, and discloses a user interface display method and electronic equipment, which are used for shortening the display time of a user interface and improving the image rendering efficiency. In the method, the electronic equipment comprises a container for bearing a client operating system and a host operating system of the electronic equipment. In the process that the container runs the first application, the container can send the generated interface drawing instruction to the host operating system, so that the host operating system executes the interface drawing instruction in the first window cache region to obtain the first image data. The host operating system can also render the first image data of the first window cache region to a layer of the first window so as to display the first user interface on the first window. Therefore, the host operating system can directly render the first image data to the image layer of the first window, only one time of memory copying is needed, the image rendering time is shortened, and the image rendering efficiency is improved.