526 results about "Pixel image" patented technology

The pattern generation method based on diffusion model fine tuning comprises the following steps: constructing a data set; the generation model comprises an edge detection model ControlNet and a stable diffusion model, the stable diffusion model comprises a text encoder CLIP, a diffusion model U-Net and an image decoder VAE, the CLIP text encoder converts an input natural language into word vector features and inputs the word vector features into a U-Net network in a submerged space, and the diffusion model U-Net and the image decoder VAE are connected with the edge detection model ControlNet and the diffusion model U-Net; the U-Net network is controlled to carry out iterative denoising on pure noise in a low dimension to generate a compressed image, and finally the compressed image is restored to an original pixel image through a decoder VAE, so that text content control image generation is realized; carrying out fine tuning training on the generative model by adopting a LoRA parameter efficient fine tuning technology; and intelligently generating a batik style pattern. The method has the advantages that the texture precision of the generated image can be improved, and the personalized style image can be generated.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

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 screen shooting traceability method, device and equipment and a readable storage medium, which are applied to the field of computer software, and comprise the following steps: encoding watermark information to obtain a watermark information sequence; embedding the watermark information sequence into the image through an R channel, a G channel and a B channel of RGB (Red, Green, Blue) to obtain an image with a watermark; and acquiring the shot image with the watermark image, and tracing to obtain watermark information based on the shot image. According to the method, the watermark is generated by using the RGB channel of the single pixel image, so that the visual concealment of the watermark image is improved, the difficulty in perceiving of human eye recognition is ensured, and the visual quality of a user is not influenced; the watermark embedding position is not influenced by an original image carrier, and watermark information can be hidden in any area of the image; and the watermark information recovery success rate is high.

The invention discloses a multisource remote sensing image surface water body extraction method based on a graph neural network. The SAR image is used as a main part, and the optical image, the digital elevation model and the local incident angle information are fused. Firstly, radiation correction, geometric correction and noise suppression are carried out on SAR data, and multi-source data spatial resolution matching is carried out. And then converting the image into a graph structure by adopting a self-adaptive Bayesian superpixel segmentation method. Then, geometric, texture and physical features are constructed for the superpixel nodes, and feature dimensions are optimized through a feature selection algorithm; and finally, constructing a classification aggregation graph convolutional neural network, wherein the core lies in a self-adaptive microaggregation mechanism of the classification aggregation graph convolutional neural network. According to the mechanism, neighborhood superpixels can be dynamically divided according to categories and feature fusion is carried out, and the recognition capability of the model on water body boundaries and regional heterogeneity is remarkably improved. According to the method, multi-source remote sensing data, superpixel graph structure conversion and an optimized graph convolutional network are comprehensively utilized, and the precision and robustness of water body extraction in the complex surface environment are effectively improved.

The invention relates to methods for inspecting a reflective surface (200) of an object arranged in a measurement chamber, with a device which has at least one 2-dimensional illumination pattern (201) for reflection on the reflective surface (200) and a plurality of cameras (203) for pixel-by-pixel recording of image information of the illumination pattern (201) reflected on the surface (200), wherein the position and orientation of the plurality of cameras (203) and of the at least one 2-dimensional illumination pattern (201) are known in a common coordinate system and the illumination pattern represents a temporally varying illumination pattern, wherein object image information is generated by applying a measuring sequence in which the cameras capture sections of the measurement chamber. The object image information is processed by means of the evaluation device, in such a way that output data is generated therefrom with regard to an object-related inspection task taking into account determined segmentation information, wherein the inspection task comprises, for example, the generation of defect information on the object and / or the calculation of surface normals for a plurality of points in at least one section of the reflective surface of the object and / or the calculation of the curvature for a plurality of points in at least one section of the reflective surface of the object, as output data. The invention further relates to a corresponding device, a calibration method and a computer program product.

The invention discloses a semiconductor photoelectric chip anomaly detection method and device and computing equipment, and the method comprises the steps: loading a first target chip image, based on a first color model, of a target semiconductor photoelectric chip, and converting the first target chip image into a second target chip image based on a second color model, the second color model comprises a red channel, a saturation channel and a brightness channel; performing superpixel segmentation on the second target chip image to obtain a target chip superpixel image, and collecting a feature vector of each target superpixel in the target chip superpixel image; predicting a plurality of target key areas in the target chip superpixel image by using the target visual large model; based on the feature vector of each target superpixel, generating a corresponding OCSVM model for each target key area; and performing anomaly detection on any target key area based on the OCSVM model corresponding to any target key area. According to the invention, accurate anomaly detection can be carried out on the single key area of the semiconductor photoelectric chip.

The invention relates to a hyperspectral image classification method based on a meta-pseudo-label multi-scale image convolution network, and the method comprises the steps: building a meta-pseudo-label multi-scale feature search image convolution network (MPL-MFSGCN), dividing the HSI into superpixel images of different scales through employing an SLIC superpixel segmentation technology, and converting the superpixel images into image structures; secondly, constructing a multi-scale feature search graph convolutional network MFSGCN architecture, optimizing a superpixel feature weight combination by using a neural network search algorithm, and enhancing the discrimination of feature expression; a semi-supervised teacher-student model sharing the same graph convolutional network is adopted to generate meta-pseudo tags, and a student network is finely adjusted to improve the robustness of the network; the key features can be automatically captured under the small sample condition, the classification precision higher than that of an advanced method is obtained compared with a common HSI data set, and the classification accuracy is effectively improved.

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 present disclosure relates to an edge detection method, an edge detection device and a computer-readable storage medium. The edge detection method comprises: obtaining a template pixel image, and determining a strong edge binary image and a weak edge binary image of the template pixel image. Based on the strong edge binary image and the weak edge binary image, a strong edge image is screened to obtain a target edge binary image, and the target edge binary image has the same size as the weak edge binary image. Connected domains are selected for the target edge binary image to obtain multiple connected domains, and the connected domains include at least one cluster composed of continuous edge pixels. Among the multiple connected domains, a target connected domain whose number of continuous edge pixels in the cluster exceeds a specified threshold is screened out. The edge image of the template pixel image is detected based on the target connected domain. Through the method provided by the present disclosure, a connected domain that meets a specified threshold can be screened according to the number of edge pixels, thereby obtaining an edge of appropriate length, thereby reducing noise interference.

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 belongs to the technical field of asphalt paving segregation detection, and particularly relates to an asphalt temperature segregation detection method, system and device. The method comprises the following steps: S1, acquiring an asphalt infrared image; s2, performing super-pixel processing on the asphalt infrared image by adopting a multi-scale morphological watershed algorithm to obtain a super-pixel image; s3, performing clustering processing on the super-pixel image by adopting a K-Means clustering algorithm to obtain a plurality of segmented regions; s4, processing each segmented region by adopting an FCM clustering algorithm to generate a fuzzy label, and merging the fuzzy label into the superpixel image to serve as a segmentation result; and S5, extracting temperatures of different points in the subareas, and judging whether segregation exists in the corresponding subareas or not according to the temperatures. According to the invention, the positioning precision can be improved, so that the asphalt temperature detection requirement in the paving process is met.

Disclosed in the present invention is a standardized process algorithm for an electronic drawing of an interior design and for improving an AI recognition rate. The standardized process algorithm comprises: on the basis of an actual size and an aspect ratio, which are included in vector data of an electronic drawing, calculating a scaling ratio of the vector data of the electronic drawing that best matches the size of an A4 standard template; selecting preset vector information parameters on the basis of the magnitude of the scaling ratio and the degree of richness of vector information, and performing data type conversion by means of central template matching, so as to convert an image of the vector data into pixel images; performing image information metric calculation on data of the pixel images obtained by means of conversion and taking same as the degree of image information richness, and classifying the pixel images into a high-information-density image and a low-information-density image in view of the degree of richness of vector information; adaptively selecting an interpolation algorithm for the low-information-density image by means of a self-defined similarity evaluation function to perform scaling processing; performing cropping processing on the high-information-density image by means of an overlapping sliding window method; and converting, in a unified manner, the processed images into an input size required by an AI recognition model.

The invention relates to the technical field of artificial intelligence and finance, and provides an artificial intelligence-based text wordart method, device, equipment and medium, on one hand, a target text is rasterized based on a text wordart model to obtain a target raster image text, a vector text layer is converted into a pixel layer, and the pixel layer is converted into a pixel layer; the text is not limited by language types, the problem that the Chinese language structure is complex is avoided, and the text-to-graph problem is converted into the graph-to-graph problem; on one hand, a large language model is used for recognizing regional coordinates used for semantization in raster image characters and expanding style cue words, and the controllability degree of text art generation is improved; and on the other hand, the discriminator is utilized to guide the diffusion model to generate a font which can be obviously recognized, and meanwhile, through an adversarial training mechanism, the generated wordart has the advantages of clear font recognition and artistic semantic change, so that the style and the font are highly fused and balanced, and the problems of low efficiency and poor quality of the text wordart are solved.

A system for generating a pixel map of a yard or facility in substantially real-time. The pixel map may be generated on-the-fly with virtual slots based on various vehicles and containers entering and exiting the yard or facility. In some cases, the pixel map may be utilized to track assets within the facility even if the physical identifier of the asset is unknown or the asset transitions between field of view of various image devices associated with the system.

The invention discloses a power system software vulnerability detection-oriented code multi-modal fusion representation learning method, which comprises the following steps of: firstly, obtaining four modals of a text, a pixel image, an attribute graph and complexity measurement of a power system software code through program analysis; then, modal interaction feature extraction is carried out by using dual-channel convolution and a graph neural network, and information transmission between modals is enhanced; and finally, designing a multi-modal collaborative fusion framework, performing reconstruction coding on the features based on feature mapping and cross-modal reconstruction loss, and fusing the multi-modal feature representation of the power system software code into a unified representation for constructing a vulnerability detection model for the power system software. According to the method, the completeness of modeling data representation of the power system software vulnerability detection model is enhanced through a code multi-modal interactive fusion representation learning method.

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 discloses an LCD photocuring 3D printer and a method for improving the resolution of the LCD photocuring 3D printer, the angle of light passing through a pixel opening area of an LCD screen is changed by adjusting the angle of a reflector group, the position of a curing light beam in a printing area (photocuring glue) is further influenced, so that the offset of a pixel image is realized, and when the offset is carried out for n times, the position of the curing light beam in the printing area (photocuring glue) is changed. Compared with the prior art, n times of pixel points are added on the basis of an original image, namely, the resolution ratio of the LCD is expanded in a multiplied mode, the effect of improving the resolution ratio of the LCD photocuring 3D printer in a multiplied mode is achieved, meanwhile, the sufficiency and compactness of an exposure area are improved, and the printing yield is improved.

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.

Methods, apparatuses, and software are disclosed for multilayer metrology. One method includes obtaining image data of an object with an SEM system, with the image data acquired at multiple landing energy levels. A composed image is generated by performing pixel-by-pixel image processing of the image data. A metrology characteristic is determined from the composed image and metrology is performed on a feature based on the metrology characteristic.

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 light field image angle super-resolution method based on large kernel convolution attention, and relates to the technical field of image processing, and the method comprises the steps: converting an input sub-aperture image into a macro-pixel image, and extracting the shallow features of a light field image through employing a convolutional neural network; based on the shallow layer features of the light field image, extracting deep layer features of the light field image through a plurality of cascaded multi-scale mixed attention modules; performing angle down-sampling, channel expansion, Pixel Shuffle up-sampling and spatial convolution processing on the deep features of the light field image in sequence to generate a super-resolution macro-pixel image, and converting the super-resolution macro-pixel image into a sub-aperture image. The problems that an existing method is complex in calculation process and low in calculation efficiency are solved.

An image sensor comprising a semiconductor substrate, a plurality of photodiodes, a multilayer reflective stack, and a dielectric layer is disclosed. The plurality of photodiodes is disposed within the semiconductor substrate and includes a first photodiode and a second photodiode adjacent to the first photodiode. The multilayer reflective stack comprises a first material having a first refractive index and a second material having a second refractive index. The dielectric layer has a third refractive index and is disposed between the first photodiode and the multilayer reflective stack. The first material is disposed between the second material and the dielectric layer. The first refractive index is greater than the second refractive index and the third refractive index.

Single pass printing methods designed to reduce or prevent unwanted image distortion and / or image defects when printing on a ball. In some embodiments, the methods can comprise printing ink droplets based on a distorted pixel map comprising a plurality of pixel areas distorted in at least one of a width direction and a height direction, wherein select pixel areas are removed from the distorted pixel map. In some embodiments, a distorted pixel map can comprise a plurality of undistorted pixel areas and a plurality of pixel areas distorted in at least one of a width direction and a height direction.

In certain embodiments, an ophthalmic system images an eye region comprising at least one eye. The system includes a camera system and a computer. The camera system includes cameras that yield image portions of the eye region. Each camera is located at a position relative to the eye region and yields an image portion. The computer receives the image portions from the camera system. A first image portion is provided by a first camera, and a second image portion is provided by a second camera. The computer identifies identify target pixels of the first image portion, where the target pixels image a location of the eye region; determines image information of correction pixels of the second image portion, where the correction pixels image the same location of the eye region; and corrects the target pixels using the identified image information.

The invention discloses a light field image spatial super-resolution method based on local and global complementary information, which relates to the technical field of image processing, and comprises the following steps: extracting an initial light field feature map from an input low-resolution light field image by using a convolutional layer; utilizing a local-global space angle complementary information fusion module to extract local-global space angle complementary information from the initial light field feature map to obtain depth features of the light field image; and obtaining a high-resolution light field image by using a convolutional layer and a pixel recombination operation based on the depth feature of the light field image. According to the method, the problems that the complementary relationship between different sub-aperture images and different macro-pixel images is ignored and the global complementary information in the light field image is not fully utilized in the existing method are solved.

The embodiment of the invention discloses a stray light testing method and device for a vehicle-mounted image acquisition module, equipment and a medium. Comprising the following steps: acquiring a pixel image, a first brightness image and a second brightness image corresponding to a preset light source; wherein the pixel image is acquired by a to-be-detected vehicle-mounted image acquisition module, and the to-be-detected vehicle-mounted image acquisition module comprises a first lens; the first brightness image is collected by a first brightness meter composed of a preset sensor and a second lens, and the second brightness image is collected by a second brightness meter composed of a preset sensor and a first lens; and determining the energy level of stray light in the to-be-detected vehicle-mounted image acquisition module based on the pixel image, the first brightness image and the second brightness image. According to the scheme, the energy level of the stray light is directly determined by adopting a method of matching the brightness meter with the vehicle-mounted image acquisition module to be detected instead of using a dimmer and approximately calculating the energy level by adopting a pixel value and the dimmer, and any intermediate conversion and attenuation process does not exist, so that the accuracy of the magnitude of the stray light energy is improved.