1194 results about "Image area" patented technology

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention discloses a sludge treatment automatic monitoring method based on computer vision, and relates to the technical field of sludge treatment automatic monitoring, and the method comprises the following steps: collecting sludge image information through computer vision, extracting an edge integrity index and a brightness distribution index, and constructing a fusion judgment function based on the time sequence change of the two indexes, determining whether a thin water film exists on the sludge surface or not according to an output result of the fusion judgment function; under the condition of determining that a thin water film exists on the sludge surface, acquiring a pixel highlight gradient polymerization rate, a texture missing fluctuation frequency and an image saturation nonlinear deviation value of a corresponding image region, and establishing a specular reflection image feature recognition matrix; by constructing a multi-dimensional image feature recognition and dynamic regulation and control mechanism, the problem of image misjudgment caused by thin water film mirror reflection in the sludge dewatering process is solved, and accurate recognition and automatic closed-loop control of the real water-containing state of the sludge are achieved.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.

An encoder includes circuitry and memory coupled to the circuitry. In operation, the circuitry encodes subpicture information in which a horizontal position and a vertical position of a region of a subpicture are represented in a unit of a coding tree unit (CTU). The subpicture is a rectangular region in a picture. The horizontal position is represented by a first position of a first CTU in the subpicture, the first position being relative to a left end of the picture. The vertical position is represented by a second position of a second CTU in the subpicture, the second position being relative to a top end of the picture.

The invention discloses a film processing quality evaluation method and system, and the method comprises the steps: obtaining and preprocessing a processed film collection image, and obtaining a film gray image; determining a gray value of each pixel point in the film gray image, and performing clustering analysis on the pixel points based on the gray value to obtain a plurality of abnormal image areas; determining multi-dimensional image features in each abnormal image region, and analyzing abnormal conditions of each abnormal image region based on the multi-dimensional image features to obtain an abnormal coefficient; determining the number of the abnormal image areas, and determining a comprehensive quality evaluation value of film processing based on the number of the abnormal image areas and the abnormal coefficient; and determining a quality evaluation grade of thin film processing based on the comprehensive quality evaluation value, and evaluating the processing quality of the thin film according to the quality evaluation grade. According to the invention, comprehensive quantitative evaluation of the processing quality of the film can be realized, the production efficiency of the film is improved, the quality risk is reduced, and the production of high-quality film products meeting standards and customer requirements is ensured.

The method is mainly applied to the technical field of artificial intelligence processing. The invention discloses a face and license plate privacy protection video processing method, device, equipment and medium, and the method comprises the steps: extracting input video data to obtain a video frame sequence which comprises a plurality of video frames; the video frame sequence is input to a pre-trained face and license plate detection model for target detection, so that a target video frame containing a target image area is determined, position information of the target image area in the target video frame is obtained, and the target image area is a face area or a license plate area; based on the position information of the target image area, performing fuzzy processing on the target image area in each target video frame; and generating a video after fuzzy processing based on the video frame after fuzzy processing. According to the method and the device, the privacy protection and the target detection are collaboratively optimized, so that the efficiency and the accuracy of target detection are improved while the privacy security is guaranteed.

The invention relates to the technical field of image texts, in particular to an image text alignment method based on a multi-modal large language model, which comprises the following steps of: segmenting an image into local areas and encoding the local areas into visual feature vectors by adopting a pre-trained visual encoder; visual and text features are mapped to the same dimension space through a learning projection layer, an InfoNCE loss function is adopted to maximize the similarity of a positive sample pair, dynamic interaction between text query and an image area is achieved through a cross attention mechanism, language module parameters are frozen to pre-train a visual encoder, an overall module is combined to be fine-tuned, and text query is completed. An error case is analyzed, and a verified and improved alignment module is obtained through a data enhancement optimization module. The method solves the problems that a traditional image text alignment method is incomplete in manual feature extraction and low in precision, an existing deep learning model is limited in cross-modal semantic understanding ability, and the generalization ability is poor when facing long-tail data.

The present disclosure describes a system for detecting errors in outputs generated by GenAI models. The system includes a knowledge base of domain-specific evaluation rules and historical error patterns stored. The system parses each received GenAI output, then applies a recommendation algorithm to produce a weighted metric profile defining evaluation criteria and associated rules. Further, the system generates an embedding for the output, calibrates criterion weights by comparing the embedding to domain vectors in the knowledge base, and executes a neural network-trained on public benchmarks and enterprise-annotated examples—to yield per-criterion scores and pass-fail explanations. The processor identifies breaches by comparing scores to thresholds, creates structured error stubs, and localizes errors by projecting attention maps onto text tokens or extracting gradient-based image regions. Finally, it updates the knowledge base-adding new error patterns, adjusting rule weights, and revising thresholds—to continuously refine the evaluation process.

The invention discloses a fan abnormity monitoring method, device and equipment based on image recognition and a medium, relates to the technical field of image recognition, is applied to a wind turbine generator cabin, and comprises the following steps: acquiring an original image sequence in the wind turbine generator cabin in a first candidate image area in real time, eliminating environmental interference factors in the original image sequence of the first candidate image region to obtain a target image sequence; extracting visual features in the target image sequence of the first candidate image area, and identifying visual anomalies in the target image sequence of the first candidate image area based on the visual features of the first candidate image area; and when the visual anomaly is identified in the target image sequence of the first candidate image area, outputting alarm information containing the visual anomaly of the first candidate image area. The technical problem that a current fan monitoring scheme is poor in monitoring effect is solved.

The invention discloses a brain image area label generation method based on standard space inverse deformation reflection, and relates to the technical field of medical image processing and computer vision. The method comprises the following steps: performing skull stripping and resampling preprocessing on an individual space target brain image, performing rigid, affine and nonlinear cascade registration on the preprocessed brain image and a standard space template, and solving a forward deformation field and an inverse deformation field; calling an inverse deformation field to reversely propagate standard space anatomical atlas labels to an individual space, and resolving label conflicts to obtain an initial label graph; generating each modal coarse label graph through the registration matrix, and performing weighted fusion according to a confidence coefficient weight to obtain a consistent coarse label set; quality control is executed based on anatomy prior, an individualized label is obtained after topological repair, and finally a label, metadata and an uncertainty evaluation report are exported. According to the method, automation and standardization of brain image tag generation are realized, and the accuracy, consistency and generalization ability of tags are improved.

The invention provides a vehicle-mounted instrument driving record panoramic image fusion three-dimensional projection method and system, and relates to the technical field of vehicle-mounted display, and the method comprises the steps: obtaining original panoramic image data collected by cameras around a vehicle and real-time driving state information of the vehicle; distortion correction and multi-view splicing processing are carried out on the original image to generate a spliced panoramic image; determining the type of a driving scene according to the real-time driving state information, setting corresponding three-dimensional viewpoint parameters including viewpoint height, viewpoint distance and pitch angle, performing three-dimensional space coordinate transformation on the spliced panoramic image based on the parameters, and generating a three-dimensional aerial view image adaptive to the current driving scene; an obstacle target is extracted, the boundary of an image area of the obstacle target is recognized, height stretching rendering is carried out at the corresponding position of the three-dimensional aerial view image, and a three-dimensional panoramic projection image containing an obstacle three-dimensional identifier is generated; and finally adaptively outputting to a vehicle-mounted instrument panel display screen for real-time display. The visual angle can be dynamically adjusted according to different driving scenes, the stereoscopic perception of the obstacle is enhanced, and the driving safety is improved.

The invention relates to the technical field of point location labeling, in particular to a self-adaptive load balancing point location labeling method and system based on image processing, and the method comprises the following steps: obtaining an image source division region, extracting an edge pixel number texture direction number color channel standard deviation, generating region detail redundant structure distribution information, and monitoring node state data. The method comprises the following steps: constructing a node resource load state mapping set, matching according to a regional detail redundancy ratio and a node state, establishing a task allocation relationship, extracting a boundary gray derivative, screening a boundary stable candidate point location set, calling point location coordinates, sending the point location coordinates to corresponding nodes, executing target verification, writing in an image frame, and generating a labeling result. Quantitative extraction is performed on distribution among image region edge pixels, texture directions and color channels, and task assignment is accurately paired in combination with image structure complexity and node states, so that resource mismatching and processing retardation are avoided, and task distribution accuracy is improved.

The invention discloses a Shack-Hartmann wavefront detector centroid calculation method and related equipment, and relates to the field of optical measurement, and the method comprises the steps: obtaining a light spot array grayscale image, detecting the local maximum value of each light spot, and dividing sub-aperture image regions corresponding to micro lenses one by one according to the local maximum value; extracting a gray matrix of each sub-aperture image region, respectively determining one-dimensional regions of a light spot main lobe in row and column directions, and intersecting to obtain a main lobe region; a main lobe area is deducted from the sub-aperture image area to form a threshold calculation area, and the maximum gray value of the threshold calculation area is selected as a threshold; and performing threshold reduction on pixels in the sub-aperture image area, setting a negative value to zero, and calculating the position of the mass center of the light spot based on the processed image. According to the method, sub-aperture adaptive threshold and local background removal are realized, weak light spot information loss caused by a unified threshold is avoided, and the precision of centroid calculation and wavefront restoration is improved.

A method of video processing is provided. The method may include inputting attribute data and a sparse-depth map associated with an image area into a sparse-depth completion network. The method may include generating a refined dense-depth map based on the attribute data and the sparse-depth map using the sparse-depth completion network. The method may include performing a three-dimensional (3D) reconstruction procedure based on the refined dense-depth map to generate a point cloud of the image area. The method may include performing a triangular-meshing procedure to generate a mesh model based on the point cloud of the image area. The method may include performing a texture-mapping procedure based on the mesh model and the attribute data to generate a textured mesh of the image area. The method may include performing a vertex-normal procedure based on the textured mesh to generate a 3D representation of the image area.

The invention discloses a method for performing visual inspection on surface forming quality characteristics of a welding seam by utilizing three-dimensional reconstruction, which comprises the following steps of: S1, performing uniform-speed scanning on the welding seam along the direction of a welding bead by using linear structured light emitted by a linear laser, and performing image acquisition on linear structured light stripes formed on the surface of the welding seam by using an industrial camera; s2, converting the acquired RGB image into a gray level image frame by frame, and carrying out image distortion correction and image denoising processing; s3, carrying out ROI (Region of Interest) positioning on the formed line structured light stripe image on the surface of the welding seam by adopting a pixel point gray level distribution calculation method; s4, carrying out image segmentation on the ROI region of the weld line structured light stripe image; and S5, carrying out sub-pixel-level center line extraction on the ROI of the weld line structured light stripe image by adopting a gray extreme value weighted centroid method. Automatic detection of welding seam forming quality characteristics such as laser welding and electric arc welding can be achieved, and the operation efficiency of a production line and the welding seam quality detection precision can be greatly improved.

The invention discloses a task execution method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps that a task prompt word and an interface image are obtained, the task prompt word is used for describing a to-be-executed interaction task, and the interface image is divided into at least two image areas; acquiring image feature representations corresponding to the at least two image areas and text feature representations corresponding to the task cue words; identifying the relevance between the image feature representation and the text feature representation corresponding to the at least two image regions, and determining at least one candidate image region of which the relevance meets the requirement; and generating a task instruction based on the at least one candidate image area, wherein the task instruction is used for instructing execution of an interaction task on the first interface. In combination with the text feature representation corresponding to the task prompt word, redundant feature input is reduced, and the correlation between the candidate image area and the interaction task is improved, so that the accuracy of the task instruction and the execution efficiency of the interaction task are improved.

Systems and methods for image processing (e.g., image extension or image uncropping) using neural networks are described. One or more aspects include obtaining an image (e.g., a source image, a user provided image, etc.) having an initial aspect ratio, and identifying a target aspect ratio (e.g., via user input) that is different from the initial aspect ratio. The image may be positioned in an image frame having the target aspect ratio, where the image frame includes an image region containing the image and one or more extended regions outside the boundaries of the image. An extended image may be generated (e.g., using a generative neural network), where the extended image includes the image in the image region as well as generated image portions in the extended regions and the one or more generated image portions comprise an extension of a scene element depicted in the image.

The invention provides an orthodontic skull lateral film cervical vertebra image segmentation method and system. According to the method, an orthodontic skull side position film image to be processed is obtained, a target image area containing a second cervical vertebra, a third cervical vertebra and a fourth cervical vertebra is determined, then image preprocessing is carried out on the target image area to obtain a standardized input image, then the standardized input image is input into a CNN-Transformer fusion segmentation network for segmentation reasoning, and the image is obtained. According to the method, the centrum segmentation masks of the second cervical vertebra, the third cervical vertebra and the fourth cervical vertebra are output, and finally, the segmentation result is output based on the centrum segmentation masks, so that the segmentation result is output based on the centrum segmentation masks, and a repeatable and traceable objective basis can be provided for subsequent morphological parameter extraction and bone age / maturity quantitative evaluation; the subjective deviation and time cost of manual sketching are practically reduced, and the segmentation consistency and availability under the cross-individual and cross-equipment condition are improved.

The invention discloses an electronic component intelligent classification method and system based on design data, and the method comprises the steps: obtaining a design file of a printed circuit board and a corresponding board image; analyzing based on the design file to obtain bit numbers and layout pose information of the components, determining image areas of the components in the board surface image according to the bit numbers and the layout pose information, and generating standardized component images corresponding to the components one by one; extracting feature vectors from the standardized component image to form a component feature set; executing clustering based on the component feature set to obtain a plurality of category clusters; and displaying representative samples of the category clusters, receiving semantic annotations of users on the category clusters, establishing a mapping relationship between the clusters and the categories, and outputting classification results of the components based on the mapping relationship. According to the technical scheme, the electronic component classification efficiency is improved.

The invention discloses an intelligent auxiliary method and system for graphic annotation depth analysis, and particularly relates to the field of image processing and image distinguishing, and the method comprises the steps: collecting an image sequence containing a target object, and synchronously recording environment data which comprises an illumination condition, a shooting visual angle parameter and a shielding position; combining the image sequence with environmental data to form an input substrate; according to the scheme, multi-source information such as image area complexity, texture features and edge curvature is subjected to comprehensive modeling in a sheltered environment, a structure response mapping and boundary consistency scoring mechanism is constructed, and credible completion candidate areas are dynamically generated and screened, so that verifiable completion and graphic marking consistency guarantee of sheltered targets is realized, and the accuracy of the sheltered targets is improved. Therefore, the problem that the authenticity of the graphic annotation data is difficult to maintain due to target missing of the existing graphic annotation mechanism is solved.

The invention relates to the technical field of robots, and discloses an obstacle sensing method and a storage medium, and the method comprises the steps: obtaining image data of an environment in front of a mobile robot and three-dimensional point cloud data generated through laser radar scanning; pixel-level semantic segmentation is carried out on the image data, and a semantic segmentation map with each pixel having a semantic tag is generated; projecting each three-dimensional point in the three-dimensional point cloud data to a semantic segmentation map based on a calibration relationship between a camera and a laser radar, and generating a semantic point cloud with a semantic tag; for a detection blind area of the laser radar in front of the robot, generating a virtual three-dimensional point cloud in the detection blind area by using obstacle semantic information corresponding to an image area of the detection blind area in the semantic segmentation map; and determining the obstacle point cloud based on the three-dimensional point cloud of which the semantic label is the obstacle category in the semantic point cloud and the virtual three-dimensional point cloud. According to the invention, the obstacle detection precision in a complex environment can be improved.

The invention provides a robot operation task control method, device and equipment based on a visual language action model, a robot and a medium, and relates to the technical field of sensors and robots. The method comprises the following steps: predicting weights between visual angles of corresponding visual image data based on global feature vectors corresponding to the visual image data of a plurality of visual angles of a robot; according to the plurality of local feature vectors corresponding to each piece of visual image data, predicting the weight in the visual angle of the corresponding image area; fusing the feature vectors of the reserved areas in the visual images in combination with the inter-view-angle weight and the in-view-angle weight of each image area to obtain a fused feature vector; and inputting the fusion feature vector into a robot operation task control strategy network based on a visual language action model to obtain an operation task control instruction. The motion execution precision can be improved, the computing power burden is reduced, the computing efficiency is improved, and the task success rate of complex and fine tasks is improved.

Embodiments of the present disclosure provide an image compression method and apparatus, a computer device, and a computer readable storage medium. The method comprises: according to channel values of pixel points in an image to be compressed, determining a plurality of image feature regions matched with a target object in the image to be compressed; according to compression parameters corresponding to each image feature region, respectively compressing each image feature region to obtain compressed image regions; and generating a compressed image according to the compressed image regions. The embodiments of the present disclosure can improve the definition of the compressed image.

The invention relates to the technical field of image processing, and particularly discloses a valve well gas leakage detection system based on image region segmentation. The system comprises an image acquisition module, a preprocessing and enhancement module, a multi-scale feature extraction module, a semantic segmentation network module, a false leakage suppression module and a decision output module, through multi-modal image fusion, semantic segmentation and texture consistency verification, accurate identification and positioning of a leakage area are realized, false leakage interference is suppressed, and the detection reliability is improved.

The invention discloses a parking lot license plate fee evasion prevention visual identification method and system, and relates to the technical field of intelligent parking management. The method comprises the following steps: step 100, synchronously obtaining a lane panoramic image and 3D spatial data of a corresponding object; step 200, determining whether there is a multi-vehicle foldover area in the panoramic image based on double-feature constraints of license plate semantic features and geometric features; step 300, based on the constraint of a standard license plate template library, carrying out pixel splitting and image completion on the multi-vehicle foldover area to obtain an independent and complete single license plate image; step 400, performing character recognition on the single license plate image, and verifying the authenticity of the license plate in combination with the 3D space data; and step 500, realizing multi-license-plate independent charging and vehicle-by-vehicle gate opening control based on an identification result, and storing related data for tracing. According to the method, the problems of misrecognition and missed recognition fee evasion caused by multi-vehicle foldover are solved, license plate counterfeiting fee evasion is prevented, and the benefits of the parking lot are guaranteed.

The invention relates to the technical field of video compression, in particular to an AI-based video compression platform, which comprises a key target detection module, an image region division module, a coding level setting module, a parameter adjustment and analysis module and a coding result integration module. According to the method, key region extraction is completed by collecting pixel color combination and contour change information in a video frame image, background and non-background region boundaries are delimited, a region attribute labeling result is constructed, a region distribution classification relation mapping coding level is established, and reference frame and prediction interval configuration is extracted. Real-time adjustment of compression levels is realized by performing normalized combination analysis on textures and motion change trends of different regions, and region fragments under different compression levels are recombined and subjected to unified code stream processing, so that coding resources can be dynamically allocated on the basis of accurately identifying contents in a compression process; the problems of inaccurate target positioning, fixed compression configuration, delayed content response and the like in an existing compression system are effectively solved.

The invention relates to a reconstructed image anti-aliasing processing method based on an adaptive sampling rate, belongs to the technical field of CT (Computed Tomography) reconstructed image processing, and solves the problem that the existing anti-aliasing processing method for a CT reconstructed image is poor in effect. The anti-aliasing processing method for the reconstructed image comprises the following steps: preprocessing the reconstructed image to obtain a to-be-processed image; performing edge extraction on the to-be-processed image based on a preset gradient threshold and a preset Sobel operator, and segmenting the to-be-processed image into a to-be-replaced image region and a reserved image region; determining an adaptive sampling rate based on the horizontal direction edge pixel connection number and the vertical direction edge pixel connection number of all sawtooth vertex pixels in the to-be-processed image; determining a pixel value of each pixel in the to-be-replaced image region based on the adaptive sampling rate and the to-be-processed image, and obtaining a replaced image region; and combining the replaced image area and the reserved image area to obtain an image after anti-aliasing processing. And the anti-aliasing effect of the CT reconstructed image is improved.

Image processing apparatuses, image pickup apparatuses, image processing methods, and storage media are provided herein. One or more image processing apparatuses may include one or more memories storing instructions, and one or more processors that, upon execution of the instructions, operate to detect a first object and a plurality of second objects from an image, obtain information on a distance of each of the first object and the plurality of second objects, and determine an image area from the image including a first area including the first object and a second area including at least one second object selected from the plurality of second objects according to the information on the distance.