276 results about "Image layer" patented technology

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 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.

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 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 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.

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.

The invention relates to the technical field of defect detection, in particular to a semiconductor chip surface defect detection method based on deep adversarial supervision, which comprises the following steps: acquiring a chip surface image, and inputting a pre-constructed defect detection model to obtain a defect segmentation result of the chip surface image; the defect detection model comprises an encoder, a decoder and a discriminator; the encoder is used for extracting multi-scale features of the chip surface image; the decoder is used for recovering the spatial resolution of the image layer by layer based on the multi-scale features and outputting a defect segmentation result of a corresponding scale; the discriminator is used for discriminating the difference between the defect segmentation result output by the decoder at the corresponding scale and the real label image at the corresponding scale; the method comprises the following steps: updating parameters of an encoder and a decoder by calculating semantic segmentation loss; and then adversarial training is carried out in combination with the discrimination result of the discriminator, and the parameters of the encoder, the decoder and the discriminator are jointly optimized, so that iterative updating of the parameters of the defect detection model is realized. The defect detection precision can be improved.

A system and method for detecting a cybersecurity issue in a software container layer and mitigating the same is presented. The method includes: detecting a software container including a plurality of layers; associating a first layer of the plurality of layers with a first image of the software container, and associating a second layer of the plurality of layers with a second image of the software container; inspecting each of the plurality of layers for a cybersecurity issue; detecting a cybersecurity object on the first layer, wherein the cybersecurity object indicates the cybersecurity issue; initiating a remediation action on the first image, in response to detecting the cybersecurity object on the first layer.

Methods and systems are provided herein for generating a morphology-based recomposition based on 3-dimensional input image data including an image stack of 2-dimensional (2D) images, the morphology-based recomposition being a final image generated by performing one or more white top hat (WTH) transforms on each 2D image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated PSF and image data.

The invention provides a circuit board defect detection method and system based on deep learning, and the method comprises the steps: firstly obtaining a layered image set of a to-be-detected circuit board, which comprises a welding spot imaging layer, a line imaging layer and an element imaging layer; calling a pre-trained circuit board defect hierarchical detection model, inputting the hierarchical image set into a model defect pre-association layer, and generating a hierarchical defect association map which marks the position mapping relation of the same defect in different imaging layers; on the basis of the layered defect correlation map, extracting defect area exclusive features of each imaging layer to form a layered exclusive defect feature set, and inputting a model hierarchical feature enhancement layer to generate a hierarchical enhanced defect feature set. And finally, calling the model scene adaptive classification layer to carry out defect type matching, and generating a circuit board defect detection report containing defect types, hierarchical positions and association relationships in combination with the position mapping relationship, so that the circuit board defects can be comprehensively and accurately detected, and the detection efficiency and quality are improved.

The invention relates to an abnormal phased array radar echo image restoration method based on a generative adversarial network, and the method mainly comprises the following steps: carrying out the preprocessing of radar echo image data, and generating a missing region mask; then, constructing a generative adversarial network model based on a double-flow encoder-decoder, extracting features of input image data and masks through a double-flow encoder, and performing feature fusion; then, a strong echo color correction module and a local detail enhancement module are utilized to perform enhancement processing on the fusion feature map, and detail textures of a shielding region and a strong echo region are restored; reconstructing the image layer by layer through a double-current decoder, and generating a repaired complete image; and finally, carrying out adversarial training by adopting a double-flow discriminator, optimizing a generator, and ensuring the authenticity and accuracy of the repaired image. According to the method, the abnormal region, especially the strong echo region, in the radar echo image can be effectively restored, and the image restoration precision and efficiency are improved.

The invention provides a real-time video image stabilization method and system for performing intra-frame motion compensation based on an IMU (Inertial Measurement Unit). The method comprises the following steps: S1, preprocessing data; s2, track smoothing processing is carried out; s3, performing intra-frame motion compensation: S3.1, initializing line exposure delay time; s3.2, calculating the time difference between the current vth line and the middle line exposure in the current frame image; s3.3, acquiring the smoothed virtual camera pose of the current frame and the real camera pose of each layer; s3.4, acquiring a motion compensation matrix for compensating the line of image to the virtual pose of the frame of image; s3.5, acquiring an image frame perspective transformation matrix according to the camera internal reference matrix K and the motion compensation matrix; s3.6, performing intra-frame motion compensation on the image layers to obtain an initial motion compensation matrix queue; s3.7, carrying out black edge removal processing on the motion compensation matrix queue; obtaining a motion compensation matrix queue after black edge removal; and S4, image transformation output. The system comprises a data preprocessing module, a track smoothing module, an intra-frame motion compensation module and an image transformation output module. The operation efficiency is improved, and the anti-shake real-time performance is improved.

The invention discloses an aluminum foil surface defect detection system based on visual identification, and relates to the technical field of aluminum foil detection, and the system comprises a data acquisition module which is used for obtaining image data and environment data in a detection area; the data processing module is used for processing the obtained image data to obtain a region fusion layer; the image layer optimization module is used for carrying out image layer boundary optimization on the obtained region fusion image layer and splitting the region fusion image layer after boundary optimization to obtain an aluminum foil feature image layer; the defect identification module is used for carrying out surface defect analysis on the aluminum foil characteristic pattern layer and outputting a surface defect analysis result; according to the method, environment data including various standards are set, dynamic matching and layer fusion are performed during detection, and a self-adaptive image analysis reference is constructed, so that imaging quality fluctuation caused by environment illumination change is reduced, and the detection accuracy and reliability in a changeable industrial field are remarkably improved.

Methods, systems, and non-transitory computer readable storage media are disclosed for generating visualizations of mask correlations for a layer of a digital image. The disclosed system determines one or more bounding boxes corresponding to one or more hidden areas or one or more visible areas of a layer of a digital image according to a raster mask or a vector mask corresponding to the layer. The disclosed system determines display attributes for the one or more bounding boxes in response to determining that the one or more bounding boxes correspond to the one or more hidden areas or the one or more visible areas. The disclosed system generates, for display with the layer within a graphical user interface, one or more boundary highlights representing the one or more bounding boxes with the display attributes.

A system and method for detecting a cybersecurity issue in a software container layer and mitigating the same is presented. The method includes: detecting a software container including a plurality of layers; associating a first layer of the plurality of layers with a first image of the software container, and associating a second layer of the plurality of layers with a second image of the software container; inspecting each of the plurality of layers for a cybersecurity issue; detecting a cybersecurity object on the first layer, wherein the cybersecurity object indicates the cybersecurity issue; initiating a remediation action on the first image, in response to detecting the cybersecurity object on the first layer.

The invention provides an edge cloud cache configuration method and device based on tensor complementation, and relates to the technical field of containerization deployment in an edge cloud network, the method comprises the steps of decomposing mirror image layer prefetching into prediction of mirror image layer cache and mirror image layer pre-scheduling, combining tensor complementation, an iTransform model and a CP decomposition algorithm, and after the tensor is complemented, obtaining an edge cloud cache configuration result. The iTransform model captures non-stationary dependence among dimensions of tensors through an inverted time sequence attention mechanism of the iTransform model while keeping high efficiency of parameters, steady inference on current time information is realized, so that an accurate mirror image layer prefetching task is supported, a CP decomposition algorithm performs tensor reconstruction on each selectable tensor to calculate a score of each selectable tensor, and the score of each selectable tensor is calculated to obtain a pre-fetching task of the mirror image layer. And finally, taking the selectable tensor with the highest score as a target tensor. Based on the method, efficient prefetching and cache optimization of the AI micro-service mirror image layer can be realized, so that challenges caused by resource limitation in an edge cloud environment are effectively relieved.

A synthetic image device comprises an array of line focusing elements and an image layer (20) having linearly extending cells The image layer is positioned at a distance relative to the array of line focusing elements such that a limited part of the image layer becomes visible when the image layer is viewed through the array of line focusing elements. Each cell comprises at least two sub-cells, provided side-by-side in a transverse direction (T). The sub-cells of the image layer have individual visual-contrast creating items in patterns (21) forming sub-images. Composed images, each associated with a set composed by one sub-image from each cell, are visible from different viewing angles in the transverse direction. Within overlap areas (42E-G), an average pattern density of the visual-contrast creating items in at least two sub-cells giving rise to the overlapping composed images is less than a complete coverage.

A plurality of model layers of a machine-learned model can be grouped to obtain a plurality of model layer groupings based on one or more grouping criteria. For each model layer grouping of the plurality of model layer groupings, mapping information can be generated that maps the model layer grouping to a corresponding container image layer of a plurality of container image layers of a container image. Based on the mapping information, the model layer grouping can be stored to the corresponding container image layer of the plurality of container image layers of the container image.

The invention discloses an AI-based visual image layering detection system and method, and relates to the technical field of image analysis, and the method comprises the steps: obtaining a two-dimensional plane visual image of a sample, segmenting a scanning region of the sample, carrying out the statistics to obtain an irregular sub-region and a flat sub-region of the sample, carrying out the partition scanning optimization analysis of the segmented region, and carrying out the analysis of the two-dimensional plane visual image. The method comprises the following steps of: scanning a sample to obtain a high-resolution morphology graph of a flat sub-region and an optimized morphology graph of an irregular sub-region of the sample, and carrying out rigidity phase analysis on the sample after sub-region scanning optimization to obtain a rigidity distribution graph and a phase distribution graph of the sample; and fusing the high-resolution morphology graph of the flat sub-region and the optimized morphology graph of the irregular sub-region of the sample to obtain a comprehensive morphology graph of the sample, and carrying out packaging quality detection according to the comprehensive morphology graph, the phase distribution graph and the rigidity distribution graph of the sample. According to the invention, through fusion and layering determination of morphology, rigidity and phase multi-modal data of the sample, the accuracy of sample packaging quality detection is improved.

The invention discloses a shooting method and device. The shooting method comprises the steps that in response to first input, N times of shooting are carried out, N image data sets are obtained, each image data set comprises a first static image and a first video associated with the first static image, and N is a positive integer larger than 1; and in response to the second input, performing synthesis processing on the first video in the N image data sets to generate a second video, performing synthesis processing on the first static image in the N image data sets to generate a second static image, the second video comprising a content image layer and a dynamic special effect image layer superposed with the content image layer, the content image layer comprises N first videos arranged in a grid layout, the dynamic special effect image layer comprises dynamic elements in a grid special effect template, and the second static image comprises N first static images arranged in a grid layout; and associating the second video with the second static image to generate a dynamic photo of the grid layout.

The application provides an image mapping conversion method, device and storage medium. The method comprises: acquiring a first image to be processed, the dynamic range of the first image being a first dynamic range; performing dynamic range mapping on the first image based on a plurality of different mapping strategies respectively to obtain a first image layer, a second image layer and a third image layer; the mapping strategy is used to map an image from the first dynamic range to a second dynamic range, the second dynamic range being greater than the first dynamic range; the first image layer is obtained based on a first mapping strategy, the second image layer is obtained based on a second mapping strategy, and the third image layer is obtained based on a third mapping strategy; and performing image layer fusion on the first image layer, the second image layer and the third image layer to obtain a second image corresponding to the first image, the dynamic range of the second image being the second dynamic range. The technical solution guarantees the details and color authenticity of the image obtained through mapping conversion.

The application discloses a picture layer classification method and related device, and relates to the field of computers, comprising: calculating a first index value reflecting the picture layer proportion, a second index value and a third index value reflecting the spatial position relationship between the text layer and the picture layer, comparing the calculated index value with a pre-set determination condition, determining the type of the picture layer according to the comparison, and finally realizing the automatic identification task of the picture layer type in the poster, thereby providing support for realizing the automatic cropping / extension task of the canvas background and the automatic adjustment task of the picture layer adapting to the text layer layout. Since the index value used is a quantitative representation of the picture layer function in the visual level, it can reflect the differences in functional positioning, design logic and the like of different types of picture layers, so that the picture layer type identification task can be realized with high precision based on the foregoing index value. In addition, the application does not need manual marking of the picture layer type, thereby saving the labor cost.

This enables efficient layered printing by precisely positioning the lens layer relative to the image layer. [Solution] The printing apparatus divides and combines multiple original images in the sub-scanning direction to form a composite image, which is then printed with color ink to form an image layer. A solid image is printed on the image layer with transparent ink to form an intermediate layer. On the intermediate layer, a lens image consisting of multiple long lenses extending in the main scanning direction and aligned in the sub-scanning direction corresponding to the composite image is printed on the intermediate layer with transparent ink to form a lens layer. When forming the lens layer, the print head is moving in the main scanning direction when it ejects transparent ink onto the recording medium, and the transparent ink on the recording medium is cured by a curing device.

The invention relates to an image file generation method, an image generation method, an image file and a system, and the method comprises the steps: recognizing a text region in an original image, extracting an original text of an original language identifier in the text region, and recognizing the position information of the original text in the original image and a visual rendering attribute as global style data; removing an original text in the original image to obtain basic image data; translating the original text into translated texts corresponding to different language identifiers, and taking the translated texts corresponding to different language identifiers as multi-language text data; creating an initial multi-layer container structure file; filling the basic image data into a basic image layer, filling the global style data into a global style layer, filling the multi-language text data into a multi-language text layer, and packaging to obtain a target image file; the target image file is used for generating target images of different language identifiers. According to the embodiment of the invention, the generation efficiency of images identified by different languages is improved.

A method for manufacturing a synthetic image device includes providing (S10) an array of focusing elements. An image layer is arranged (S20) near the focal length of the focusing elements, whereby a synthetic image composed of a magnified portion of the image layer becomes perceptible to a viewer. The image layer includes an array of image units, each image unit associated with a corresponding focusing element. The step of arranging (S20) the image layer includes creating (S22) a corresponding image object within each corresponding image unit in the image units. These image objects make an animation perceptible, the animation comprising a series of synthetic images that are continuously perceptible as the viewing direction changes. These image objects make each of the synthetic images in the series perceptible at a corresponding perceptible depth, the corresponding perceptible depth varying between the synthetic images in the series.

The invention provides a containerized AI micro-service mirror image layer scheduling method and device, and relates to the technical field of containerized AI micro-service deploy.The method includes the steps that an optimized objective function for mirror image layer scheduling is constructed through a target node in an edge cloud network system, a mirror image layer is loaded and modeled into a sequence optimization problem with the high forward sub-modularity, and the sequence optimization problem is solved; and solving the optimization objective function by using a greedy sequence insertion algorithm to obtain a loading sequence of all mirror image layers. On this basis, a task queue of each thread in the thread set is determined in combination with a thread load minimum priority strategy; according to the method, global optimization of the loading sequence of the mirror image layer is realized, and the scheduling strategy can be dynamically adjusted according to the mirror image structure and the node resource state, so that the cold start time delay of the AI micro-service is effectively reduced, the multi-thread resource utilization rate is improved, and the deployment flexibility of the micro-service system is enhanced.

The invention provides a multi-organ collaborative diagnosis system, method, medium and device for early diagnosis of systematic diseases. The method comprises the following steps: constructing a single-organ image classification model used as a student model; constructing a semi-supervised multi-organ cooperative training model used as a teacher model; and through multi-level knowledge migration, transmitting the diagnosis knowledge of the multi-organ cooperative training model to a single-organ image classification model for clinical deployment. According to the method, a semi-supervised multi-organ cooperative training model is used as a teacher network, a model only using key organ images is used as a student network, and diagnosis knowledge in the multi-organ cooperative training model is migrated to the model only using the key organ images layer by layer through a multi-scale feature alignment mechanism, so that under the condition that only the key organ images exist, the diagnosis knowledge can be quickly and accurately obtained. The diagnostic performance of a multi-organ cooperative training model can still be achieved, and the clinical deployability and practicability of the model are greatly enhanced.

This invention relates to the field of image restoration technology, specifically disclosing an image restoration method and device based on discrete wavelet transform. An image restoration network, called Multi-Wavelet Attention Memory Neural Network (MWA-MNN), is designed, consisting of three sub-networks. These sub-networks include Discrete Wavelet Attention Blocks (DWABs) and Coordinate Channel Attention Blocks (CCABs) to extract spatial and channel feature information, effectively separating degraded image layers such as rain streaks from the image to obtain a clean image. The invention leverages the expressive power of wavelet transform for high-frequency information, thereby improving image restoration performance. The sub-networks also include Supervised Selective Kernel Blocks (SSKBs) for filtering effectively extracted features in each sub-network. This invention also proposes a memristor-based cross-array MWA-MNN circuit implementation scheme. Memristors are a novel type of non-volatile memory with faster read speeds and lower power consumption. Experimental results show that this method has good performance and versatility in various image restoration tasks.

The invention relates to the technical field of poultry determination, and provides a vertebra body size character determination method and device.The method comprises the steps that a three-dimensional CT image of poultry is obtained, and the three-dimensional CT image is segmented into a plurality of two-dimensional cross section images along the Z axis; wherein each two-dimensional cross section image is divided into a corresponding Z-axis index number; for each two-dimensional cross section image, obtaining a target two-dimensional cross section image marked with a target vertebra detection frame by using a target vertebra detection model; and determining the body size character of a target vertebra according to the target vertebra detection frame and the Z-axis index number of the target two-dimensional cross section image. According to the method, through combination of three-dimensional CT imaging layering processing and the Z-axis index number, accurate association between the two-dimensional image and the three-dimensional space position is established, and through combination of accurate positioning of the target vertebra detection model, the accuracy of vertebra body size character measurement of a lossless living body is improved.