19 results about "Membrane computing" patented technology

The invention provides a verification code verification method and device, equipment, a medium and a program product, and aims to solve the problem that an automatic script in the related technology cannot realize automatic testing of verification of a verification code of a rotary sliding block. The method comprises the steps of performing separation processing on a rotating main body and a target pattern in a verification code image to obtain a main body binary mask corresponding to the rotating main body and a target binary mask corresponding to the target pattern; based on the main body binary mask and the target binary mask, a target angle difference between the rotating main body and the target pattern is calculated, and the target angle difference is used for indicating the rotating main body to rotate to a rotating angle matched with the target pattern; generating a rotation track based on the target angle difference by using a slow motion function, wherein the rotation track comprises random disturbance and a random pause point; and controlling the rotating main body to rotate based on the rotating track to obtain a verification result. According to the invention, automatic testing of verification of the rotary sliding block verification code can be realized.

The application discloses a kind of building contour regularization-oriented polygon generation methods for remote sensing, it is related to remote sensing image recognition technical field, including: through the image instance segmentation model of training completion, real-time high-resolution remote sensing image is carried out sliding window prediction, generates candidate building instance mask, and according to candidate building instance mask calculates center point coordinate and mask area;According to mask area, generate adaptive center point distance threshold, and combine center point coordinate and mask area to exclude duplicate candidate building instance mask;Obtain elevation data that is spatially aligned with real-time high-resolution remote sensing image, carry out spatial superposition calculation to non-duplicate candidate building instance mask and elevation data, obtain elevation statistical characteristics;The application realizes the deduplication processing of candidate building instance mask, and under the unified spatial reference, combine elevation data to complete building floor value calculation and regularized building polygon generation.

A device may receive, from a robotic device, time-of-flight sensor data that includes a point cloud derived from depth images of an environment with a floor and one or more obstacles, and may shift the depth images by a number of pixels to generate shifted depth images. The device may subtract the shifted depth images from the depth images to generate final images, and may calculate Euclidean distances associated with the final images. The device may generate masks for the final images associated with Euclidean distances that are less than a search radius, and may calculate a covariance matrix based on the masks. The device may calculate eigenvectors for the covariance matrix, and may generate an occupancy grid for the environment based on the eigenvectors for the covariance matrix.

This invention discloses a knowledge distillation method for remote sensing target detection based on feature-separated attention, comprising: extracting feature attention maps from the feature maps output by the teacher network and the student network respectively; separating the foreground and background regions of the feature maps, and calculating a foreground attention mask using the feature attention map of the teacher network and a background attention mask using the feature attention map of the student network; calculating the L2 distillation loss using the foreground and background attention masks; and transferring the knowledge from the teacher network to the student network based on the L2 distillation loss. This invention can effectively select the region to be distilled, improve distillation efficiency, and improve the detection accuracy of the final lightweight target detection network without changing the student network structure or increasing computational cost.

This invention discloses an intelligent evaluation method and system for the teaching process based on learning behavior analysis, belonging to the field of intelligent evaluation technology for the teaching process. The method includes: acquiring classroom videos to extract student eye region images and establishing a mapping between time blocks and teaching segments; extracting specular and dark nucleus proxy masks and calculating the symmetrical pupil-occluded specular arc index; calculating the evidence reliability weight and eye region visibility evidence value accordingly; constructing a classroom knowledge graph containing evidence and artifact nodes, and writing the above parameters into the graph; retrieving the evidence set of the target segment, using the reliability weight to weight and aggregate the visibility evidence value to obtain an attention score and generate a traceability link. This invention effectively suppresses polarization screen reflection interference by quantifying physical artifacts and utilizing graph aggregation, achieving interpretability and traceability of the evaluation results.

This application provides a mask layout correction method, system, medium, program product, and terminal based on a combination of membrane computing and the DBSCAN algorithm. The method involves obtaining an initial layout, performing preliminary clustering on the initial layout using a preset clustering method to obtain several partitions and their corresponding density and contour features; obtaining initial density and contour parameters for DBSCAN clustering; iteratively optimizing the initial clustering parameters of the DBSCAN clustering using a preset membrane computing algorithm based on the density and contour features of each partition to obtain the optimal clustering parameters for each partition; performing DBSCAN clustering on each partition based on the corresponding optimal clustering parameters to obtain the core region, boundary region, and sparse region of the initial layout; and correcting the core region, boundary region, and sparse region based on a preset correction strategy to obtain the corrected layout of the initial layout. This improves the accuracy of mask layout correction.

The application provides a FY4B remote sensing single-channel super-resolution reconstruction method based on ESRGAN, which comprises the following steps: analyzing, uniformly cutting, scale constraining and quality detecting the resolution data of FY4B and FY3D to obtain pretreatment data; generating a maximum effective pixel intersection mask according to the pretreatment data; calculating the effective pixel ratio and performing patch screening according to the maximum effective pixel intersection mask to obtain training data; constructing an ESRGAN network structure and performing data reconstruction on the training data to obtain a predicted image. Through the establishment of paired samples of the same day, the uniform cutting alignment and scale constraint, the patch screening and mask loss calculation based on the maximum intersection of effective pixels, and the reconstruction strategy combining pixels, high frequencies and adversarial learning, the FY4B single-channel data is realized from low resolution to high resolution in detail enhancement and structure reconstruction.

The application claims an image recognition-based automatic tracking method and system for wound area of experimental mice, which regularly collects images of the wound area through a multispectral imaging device, and synchronously records parameters such as environmental light and shooting distance during the collection process to ensure data consistency, pre-processes the images using a distributed computing system, inputs the pre-processed images into a deep segmentation model trained based on a large data set to accurately identify and output a binary contour mask of the wound area, calculates wound geometric features based on the mask, dynamically generates a quantitative report including an area change rate and a healing trend coefficient, and stores all time series data in a time series database to construct a complete wound healing trajectory atlas, and supports visualization and trend analysis of the healing process. The application realizes high-throughput and automatic quantitative monitoring of the wound healing process of experimental mice, and significantly improves the objectivity and analysis efficiency of animal experiment data.

The invention relates to the technical field of integrated circuits, and discloses a frequency domain segmentation collaborative gradient driven inverse lithography method, system and device and a medium, the method comprises the following steps: obtaining a target layout image of an integrated circuit, initializing the target layout image by using initialization parameters, and carrying out multi-scale feature weighted summation to obtain a multi-scale fusion mask; performing frequency domain mapping on the multi-scale fusion mask to obtain a frequency domain component, generating a candidate region according to the frequency domain component, and performing low-frequency fusion to obtain an optimal candidate region; smoothing and mapping the optimal candidate area to obtain a mapping mask, and calculating wafer images corresponding to the mapping mask in the maximum process window and the minimum process window; calculating a loss function according to the wafer image, and performing gradient optimization on the initialization parameter according to the loss function to obtain an optimized mask parameter; and performing iterative optimization on the optimized mask parameters to obtain optimal mask parameters, and calculating an optimal mask image by using the optimal mask parameters.

The application discloses an ore crushing control method and system, electronic equipment and a readable storage medium. The method inputs a multi-scale feature map fused into a feature mapping layer to obtain a multi-scale fusion feature tensor; inputs the multi-scale fusion feature tensor into a candidate region generation layer to determine a target candidate region; inputs the target candidate region into an instance segmentation layer to determine a target instance segmentation mask; calculates a particle equivalent diameter based on the target instance segmentation mask; determines a medium crushing discharge particle size and a fine crushing discharge particle size under a P80 particle size index according to the particle equivalent diameter; calculates a material instantaneous conveying flow based on a continuous time sequence point cloud volume integration model; performs time integration on the material instantaneous conveying flow to obtain a fine crushing discharge throughput; and performs hierarchical control on ore crushing through the medium crushing discharge particle size, the fine crushing discharge particle size, the fine crushing discharge throughput and a fine crushing machine running power. The application can improve the ore crushing efficiency and the qualified rate of the crushing products.

The invention relates to the crossing field of grain storage intelligent monitoring and digital twinning technologies, and provides a grain storage multi-field coupling digital twinning-oriented temperature field prediction method and system and a storage medium, and the method comprises the following steps: S1, modeling and discretization: building a two-dimensional granary section under a unified geometric and regular grid; s2, performing multi-working-condition simulation and unified analysis rearrangement, and performing transient simulation on multi-physical-property and multi-boundary-parameter working conditions by adopting CFD (Computational Fluid Dynamics); and S3, geometric priori construction: generating a fluid domain mask and a grain pile domain mask, and calculating a signed distance field as geometric priori. Scalar conditions such as external temperature, time phase, boundary parameters and the like are mapped into channel-level scaling-offset operators through affine transformation, fine-grained regulation and control of a feature space are achieved, compared with a simple splicing method, the boundary neighborhood prediction precision is improved, the self-adaptive influence radius and assimilation intensity are designed for double domains, and the method has the advantages of being high in adaptability and high in adaptability. The boundary layer temperature correction precision is improved, and the observation information utilization efficiency is improved.

This invention discloses a method and system for segmenting welded areas based on semantic segmentation networks and depth geometric constraints, belonging to the field of welded area extraction technology. The method includes the following steps: acquiring RGB and depth images of the workpiece to be welded; acquiring rectangular detection boxes and a preliminary mask for marking the welded areas; constructing an energy model; using the energy model to perform global pixel-level optimization on the preliminary mask to determine the mask boundary, obtaining an optimized mask; calculating the gradient field, normal direction, and local curvature of the depth image in the boundary neighborhood, interpolating the depth variation curve along the normal direction to obtain an interpolation curve; and using the interpolation curve to perform depth interpolation processing on the optimized mask to obtain the three-dimensional boundary information of the weld. The method provided by this invention simultaneously considers feature representation, geometric alignment, and engineering usability, realizing a complete boundary inference process from coarse network segmentation to fine depth geometric localization.

The application discloses a kind of component automated segmentation identification method suitable for bridge point cloud, the method includes: extracting multi-view visible three-dimensional grid to bridge point cloud and rendering as two-dimensional video frame, realize the dimensionality reduction of three-dimensional point cloud;Using visual large model SAM and SAM2 obtains each component segmentation mask under each view angle;The segmentation result under diagonal view angle is projected, based on the nearest pairing algorithm of segmentation component cluster center, the segmentation result of same component on two sides is matched, according to the score of index point calculated from each view angle segmentation mask, realize the component level segmentation of bridge point cloud;Based on error point iterative repair algorithm, the segmentation result is optimized;Using multimodal large model CLIP realizes the classification identification of bridge component.The application only needs to collect the point cloud data of bridge point cloud for analysis and processing, can quickly and automatically realize the component level segmentation of bridge point cloud, based on the classification of component class based on segmentation result, realize the automated instance segmentation of bridge point cloud.

The invention relates to a chip sealing defect detection method based on a YOLO algorithm and a chip field fine-tuning large model, and belongs to the field of defect detection and computer vision. According to the method, an image enhancement module is designed, an original fuzzy or low-contrast chip image is enhanced, and the identification degree of chip surface defect key features is effectively improved through self-adaptive contrast stretching and detail sharpening processing; according to the method, a YOLO series model is deployed at the edge end, and real-time detection and type judgment of chip surface defects are achieved. For different defect categories, two processing flows are adopted: for the elargol covering defect, firstly, a YOLO image segmentation lightweight model is used for segmenting an elargol region and a chip region to obtain a corresponding mask; and then calculating the elargol coverage rate based on the mask, and judging whether the elargol coverage rate is insufficient or not. For cavity / crack defects on the surface of the chip, firstly, the YOLO target detection model is used for carrying out preliminary detection and positioning on obviously abnormal defects; and the candidate frames with low confidence coefficient and unclear boundaries are submitted to a defect detection large model which is finely adjusted in the chip field for re-checking and refined discrimination. And finally outputting a defect category and coordinate information of the defect category in the image. According to the method, through efficient YOLO target detection and a chip field fine-tuning large model, rapid and accurate chip sealing detection defect identification can be realized, the automatic detection level of a chip production line is improved, the error rate of manual detection is reduced, and the production efficiency and the product quality are remarkably improved.

This invention discloses a method for locating the centroid of a rock block based on instance segmentation and keypoint detection, belonging to the field of visual perception and automated operation of mining robots. First, this invention constructs a dataset for mining rock pile scenarios and annotates and enhances the images. Then, an improved YOLOv8 instance segmentation network is designed to identify multi-target regions in the rock pile images, outputting an instance mask for each rock block. Next, the geometric centroid and effective impact area center of the rock block are calculated based on the instance mask, and centroid keypoint supervision information is generated. Finally, a posture keypoint detection network is trained based on centroid keypoint supervision to achieve end-to-end rapid localization of the rock block's centroid, used for the generation of impact points by the hydraulic breaker arm and operational control. Experimental results show that compared with localization methods based on detection box centers or traditional image processing, as well as the original YOLOv8 segmentation-keypoint baseline method, this invention can significantly improve the accuracy and robustness of centroid localization under complex occlusion and dense stacking conditions.

The invention discloses a cloud classification method based on Cloud Sat 1B, and aims to solve the problem that cloud recognition, classification and related parameter extraction in existing Cloud Sat satellite observation data are difficult, and detection, rainfall judgment and classification of cloud clusters are realized by processing data such as radar reflectivity, temperature and cloud masks and combining cloud mask calculation and a multi-dimensional feature collaborative judgment and classification method. And structured data support is provided for cloud research.

The present application relates to the technical field of electronic manufacturing visual detection, in particular to a circuit board welding fault identification method and system based on machine vision, the method comprising: receiving a welding area image and completing gray scale conversion, Gaussian filtering and adaptive threshold segmentation, and extracting a solder joint binary mask. The geometric shape parameters of the solder joint are calculated, the ideal state category is judged through a welding state inference model, the deviation vector is obtained by comparing the ideal state and the actual parameters, and the state inversion calculation is started. The process calls a solder flowability physical simulation engine, simulates the solder spreading behavior based on temperature field and pad layout data, iteratively adjusts the surface tension coefficient and wetting angle parameters, and matches the simulation morphology and the actual morphology until the matching parameters are mapped as the welding fault type. The present application improves the attribution accuracy of circuit board welding fault identification through physical cause inversion and simulation parameter iterative matching.

The invention relates to the technical field of image processing, and particularly discloses an OCT carotid plaque segmentation method and system based on topological constraints, and the method comprises the steps: obtaining OCT carotid plaque image data; performing feature extraction and decoding on the image data based on a trunk segmentation network to generate a multi-category prediction result; performing topology constraint detection on the multi-category prediction result through a topology constraint module, identifying voxel pairs violating a preset topology constraint, and generating a topology mask based on the voxel pairs violating the topology constraint; in the training stage, topology constraint loss is calculated by utilizing the topology mask, and end-to-end training is carried out in combination with basic segmentation loss so as to optimize network parameters; in the reasoning stage, the trained network is used for directly outputting a segmentation result meeting topological constraints, natural constraints between structures are innovatively incorporated into end-to-end training, feature representation of the neural network can be enriched to a great extent, and the high requirements of medical segmentation for accuracy and efficiency are met.