44 results about "Contour matching" patented technology

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention relates to the technical field of image recognition, and discloses a lightweight system applied to label printing defect recognition. According to the system, the position of a target in a label printing image is recognized and a result is extracted by combining edge detection and a contour matching algorithm in a preprocessing stage, then, in order to improve the feature extraction efficiency and reduce the consumption of computing resources, a C-D-Conv module is constructed by using a mobile inverted residual bottleneck block, and the feature extraction efficiency is improved. In order to improve global information capture and local feature enhancement capability in image processing, an LA-LC-LM module mainly composed of self-attention, local convolution and MLP is constructed, in order to help a model to better process preliminarily extracted image low-level features in the later period, a dual-path collaborative hybrid attention mechanism DHAM is embedded in a network, and the algorithm is applied to image processing. According to the system provided by the invention, the Top-1 Acc and Param on a self-made data set Code-10 respectively reach 99.25% and 3.595 M, and the Top-1 accuracy on a public data set NEU-CLS-64 and the Top-1 accuracy on a reconstructed data set DAGM2007 * respectively reach 97.79% and 85.42%.

The embodiment of the invention discloses a texture image generation method and device, computer equipment and a storage medium, and belongs to the technical field of computers. According to the method, the texture image matched with the category and contour of the three-dimensional model can be quickly generated based on the grid data of the three-dimensional model, and an art engineer does not need to manually draw and generate the texture image, so that the generation efficiency of the texture image is greatly improved. The texture image is matched with the three-dimensional model in category and contour, so that the texture image can be ensured to be attached to the surface of the three-dimensional model, and the quality of the texture image is ensured.

The application discloses a kind of high-precision vector map automatic generation method, mainly according to laser point cloud intensity, geometric structure feature extraction lane marking, through ground filtering, ground, non-ground point cloud is segmented;Adopt block grating to convert three-dimensional point cloud into two-dimensional projection image;Using image filtering, edge extraction operator, dynamic adaptive threshold segmentation, morphological analysis and other processing extraction road marking pixel, based on point cloud image mapping relationship extraction marking point cloud;Using the point cloud contour matching method based on knowledge template library carries out marking classification.Based on deep learning convolutional neural network, road sign detection classification is carried out to road image, and the point cloud region of interest is established by image point cloud fusion;Using clustering screening candidate point cloud, based on principal component analysis carries out local projection, based on knowledge template library point cloud matching extraction traffic sign;Finally, based on GPS data, calibration, parameter fitting, vectorization, data association are carried out, and the construction of high-precision vector map is completed.

The application relates to the technical field of image detection, and discloses a scale-invariant contour matching method and device based on machine vision and a storage medium, the method comprising the following steps: extracting a target contour from a template and a query image and detecting key points; constructing a multi-scale contour context descriptor for each key point, the descriptor being composed of chord length ratios and chord angle features under multi-scale in a local neighborhood; performing feature matching, estimating scale and rotation transformation parameters according to chord length ratios and chord angles of a matched descriptor pair, and combining key point coordinates to calculate a translation parameter to generate a similarity transformation hypothesis; clustering all hypotheses as points in a high-dimensional parameter space to obtain candidate instances; for each candidate instance, fitting an accurate similarity transformation model and eliminating false matching by using a random sample consensus algorithm through matched point pairs associated with the candidate instance, and outputting final positioning parameters of a target object. The application realizes efficient, accurate and scale- and rotation-invariant contour matching.

The invention relates to the technical field of metal powder processing, in particular to an interface interlocking waveform generation method and system for an additive manufacturing technology. The method comprises the steps of obtaining multi-material model data; layering and slicing the model data to obtain a plurality of slice layers and corresponding base material contours and filling material contours; in each slice layer, a reference contour is determined from the filling material contours, and coincident contours matched with the reference contour are screened out from the base material contours to form coincident contour pairs; selecting a matrix material contour as a target contour; the target contour is processed according to set waveform parameters, and an interlocking contour with a periodic waveform structure is generated; and reversing the direction of the interlocking contour to obtain the interlocking contour of the filling material matched with the interlocking contour. According to the method, the interface shape is conveniently and flexibly regulated and controlled, and the multi-material interface bonding quality and the design and manufacturing efficiency are improved.

This invention relates to the field of metal powder processing technology, and more particularly to a method and system for generating interlocking waveforms at interfaces for additive manufacturing technology. The method includes acquiring multi-material model data; slicing the model data into layers to obtain multiple slices and corresponding matrix material and filler material contours; within each slice, determining a reference contour from the filler material contours and selecting overlapping contours from the matrix material contours that match the reference contours to form overlapping contour pairs; selecting the matrix material contour as the target contour; processing the target contour according to set waveform parameters to generate interlocking contours with a periodic waveform structure; and reversing the direction of the interlocking contours to obtain the interlocking contours of the filler material that match the interlocking contours. This method enables convenient and flexible control of the interface shape, improving the bonding quality of multi-material interfaces and design and manufacturing efficiency.

This application provides an automatic object annotation method, apparatus, electronic device, and storage medium. The method involves acquiring a first image to be annotated, containing markers and objects to be annotated; identifying markers in the first image to obtain target marker information; determining the target 3D model corresponding to the target marker information according to a pre-set correspondence between the marker information and the object's 3D model; performing contour matching on the first image to be annotated based on the target 3D model to determine the position information of the objects to be annotated in the first image; and annotating the objects to be annotated in the first image according to the position information of the objects to be annotated. This achieves automatic object annotation, reducing image annotation costs and increasing annotation efficiency.

The application discloses a visual-based contact type measuring robot adaptive measuring point relative guiding method, which comprises a 2D visual-based product image contour matching method, realizes 4-degree-of-freedom measuring point guiding of plane translation, rotation and height scaling, and is suitable for contact type six-axis cooperative measuring robot measuring point guiding measurement of products with local features; the method comprises the following steps: teaching contact type six-axis cooperative measuring robot measuring points of a product to be measured; determining a teaching 2D measuring pose, obtaining a product multi-scale contour image template under the teaching 2D measuring pose and a multi-scale image of the current product, and performing contour matching of corresponding scale levels; based on the contour matching result, calculating a first pose deviation under the teaching 2D measuring pose, transforming the first pose deviation to all teaching point positions of the teaching 2D, performing pose transformation, performing iterative updating on all measuring point positions, and obtaining all updated first measuring point poses. The application better realizes adaptive contact type measuring point measurement of products.

The application discloses a subtractive process planning method, device and equipment and a storage medium. The method comprises the following steps: acquiring contour information of each layer of a structure to be subtracted; acquiring a topological relationship between all contours contained in each layer; and identifying a corresponding relationship between contours of different layers to perform contour matching search. The contour matching search comprises finding two or more contours between different layers to establish a connection, so that the connection conforms to a structural feature of an STL model. The method is simple to operate, requires a small number of parameters input by a user, and can reduce the operation difficulty of the user. Meanwhile, NC code generation is rapid and accurate. In addition, the method is easy to integrate with a system, is used for rapidly and accurately generating subtractive process code of composite additive and subtractive machining, and controls accurate execution of a subtractive process.

This application belongs to the field of visual inspection technology for laser processing equipment, specifically disclosing a method and apparatus for detecting nozzles in a laser cutting head. The method includes: acquiring an image of the nozzle end face and an image of the optical core for the laser cutting head nozzle; based on the nozzle end face image, sequentially performing image preprocessing, nozzle outer contour matching, nozzle inner hole contour matching, and circle lookup to obtain the position of the nozzle inner circle center and the roundness detection result of the nozzle inner circle; wherein, the nozzle inner hole contour matching is performed under the constraint of the nozzle outer contour matching result; based on the optical core image, sequentially performing image preprocessing, optical core region segmentation, and circle lookup to obtain the position of the optical core center; wherein, the circle lookup is performed under the constraint of the optical core region segmentation; and based on the position of the nozzle inner circle center and the optical core center position, determining the nozzle optical core concentricity detection result. This application enables efficient and comprehensive detection of laser cutting head nozzles.

This application discloses a binocular pupil recognition method based on high-brightness reflection point anchoring and contour matching, belonging to the field of automatic pupil recognition technology. It solves the problem of low pupil recognition accuracy in the prior art under complex conditions such as eyelash occlusion, corner shadows, and partial eyelid occlusion. The method includes: first, locating high-brightness reflection points in the left and right images of the binocular system and using them as stable spatial anchor points; then, determining an adaptive segmentation threshold in the monocular view; dynamically delineating the region of interest (ROI) centered on the centroid coordinates of the high-brightness reflection points and performing binary segmentation; identifying a preliminary set of contours and filtering them based on multiple features such as contour size, area, aspect ratio, and relative positional relationship with the anchor points to obtain a reliable set of candidate contours; finally, matching the candidate contours in the left and right views and selecting the optimal pupil contour pair, thereby achieving accurate and robust pupil localization in the binocular view.

This invention relates to the field of public safety technology and discloses a method, system, robot, and medium for preventing drowning in robots. The method includes: acquiring single-beam sonar ranging data, binocular images, and robot pose data; initially screening stationary candidate targets, performing visual confirmation using binocular images, and detecting the target to be confirmed; performing geometric contour matching between the contour of the target to be confirmed and a pre-stored contour model, using an improved YOLO detection model to identify the visual features of the target to be confirmed, determining whether it is in a continuously stationary state based on the robot pose data, and recording the duration of continuous stationary state; generating a drowning prevention warning signal to issue a drowning prevention warning. This invention accurately identifies the target to be confirmed from the morphological and visual feature levels by using geometric contour matching and detection models, respectively, and provides a quantitative standard for drowning prevention warning by combining whether the target to be confirmed is in a continuously stationary state and the duration of continuous stationary state, thereby improving the efficiency of drowning prevention warning.

The invention relates to a millimeter-wave-based local amplification display device and a control method, and the method comprises the steps: obtaining three-dimensional point cloud data of a human head through the scanning of a millimeter-wave radar, extracting the contour features of the human head, calculating and generating a circular frame parameter matched with the contour of the human head, and carrying out the overlapping display of a circular frame on a display module according to the circular frame parameter. The circular frame is used for defining the boundary of the local amplification area; performing real-time image processing on the picture in the selected area of the circular frame through the FPGA chip; by applying the method, the aiming precision and the game winning rate can be improved, the global view field can be kept, the'tunnel view field 'effect can be avoided, meanwhile, self-adaptive personalized adaptation is carried out according to the shape of the head of each person, the comfort can be effectively improved, operation is convenient and fast, and fine adjustment is supported; in addition, the scheme is based on hardware-level real-time processing, low delay and high smoothness are achieved, and jamming or smear caused by processing delay is avoided; and the game fairness can be maintained through the non-intrusive design.

The invention relates to the technical field of image processing, and discloses a complex background image contour matching method based on a physical contour feature point template, and the method comprises the steps: firstly converting physical contour data into contour image coordinate data according to an image pixel equivalent, and generating a template image; gradient information is extracted through an image pyramid model and a Scharr filter, candidate feature points are screened in combination with non-maximum suppression, and feature point uniform sampling is achieved through a grid statistical method; rotating and scaling the sampled feature point set based on preset parameters to generate a multi-angle and multi-scale template set; traversing the template by adopting a sliding window method and carrying out similarity calculation on the template and the to-be-matched image; and finally, obtaining a matched contour center point through center point offset compensation. The method improves the matching efficiency on the premise of guaranteeing the completeness of feature information, effectively resists complex background and illumination change interference, solves the problem of positioning offset caused by irregular contour rotation, and has the advantages of high robustness and high precision.

The invention relates to the technical field of contour recognition, in particular to a fast contour recognition method under a complex background, and the method comprises the steps: carrying out the contour recognition training under the complex background through employing a sample image, and collecting contour attribute data, so as to determine a plurality of first clear contour segments; determining a standard contour frame based on the performance parameters of the plurality of first clear contour segments; collecting contour attribute data of the image of the target detection area, and determining a plurality of second clear contour segments so as to determine a plurality of contour matching segments; determining whether to trigger quick matching or not based on the proportion of the contour matching section; determining the adjustment amount of the contour recognition optimization parameter based on the plurality of second clear contour segments; and processing the image of the target detection area by using the adjusted contour recognition optimization parameters, and obtaining a plurality of second clear contour segments again to determine whether to trigger rapid matching. According to the invention, the efficiency and accuracy of rapid contour recognition under a complex background are improved.

The invention discloses a drainage wire clamp contour matching point cloud filtering method and system based on combination with deep learning, and relates to the technical field of point cloud filtering, and the method comprises the steps: recognizing the contour of a target object through contour matching and point cloud filtering technologies; performing dynamic filtering based on non-uniformly distributed noise, and complementing according to sparse region information; and learning feature representation of the point cloud by using a deep learning model and carrying out filtering operation. According to the drainage wire clamp contour matching point cloud filtering method based on deep learning provided by the invention, the accuracy of target object recognition is improved through a contour matching algorithm, the system has stronger adaptability to non-uniformly distributed noise points through a dynamic filtering mechanism, a sparse region is effectively processed through an information completion algorithm, and the accuracy of target object recognition is improved. The integrity of the point cloud data is improved, and efficient filtering processing of the point cloud data is realized by introducing a contour matching algorithm, a dynamic filtering mechanism and an information completion algorithm.

The present application relates to the technical field of contour recognition, and particularly relates to a fast contour recognition method in a complex background, contour recognition training in the complex background is performed using sample images, contour attribute data is collected to determine a plurality of first clear contour segments; a standard contour frame is determined based on performance parameters of the plurality of first clear contour segments; contour attribute data of an image of a target detection region is collected, a plurality of second clear contour segments are determined to determine a plurality of contour matching segments; whether to trigger fast matching is determined based on a proportion of the contour matching segments; an adjustment amount of contour recognition optimization parameters is determined based on the plurality of second clear contour segments; the image of the target detection region is processed using the adjusted contour recognition optimization parameters, the plurality of second clear contour segments are reacquired to determine whether to trigger fast matching. The present application improves the efficiency and accuracy of fast contour recognition in a complex background.

This invention relates to a millimeter-wave-based image magnification display device and control method, comprising: acquiring human head features using millimeter-wave radar; calculating and generating a safety frame parameter matching the human head contour based on the acquired human head contour data; displaying the safety frame on a display module according to the safety frame parameter; determining whether there is input for adjusting the size and position of the safety frame, and if so, making the corresponding size and position adjustments, proceeding to the next step after adjustment; otherwise, directly proceeding to the next step; and the display module performing local magnification processing on the area of ​​the image selected by the safety frame. This invention incorporates millimeter-wave radar and FPGA into the display, mainly solving the problem of millimeter-wave radar identifying the distance between the human body and the display, mapping the area that needs to be magnified based on the size of the human head, and then using FPGA algorithms to magnify the selected area, thereby improving the user's visual experience and effectively solving visual fatigue.

The invention discloses an efficient collaborative navigation method based on contour matching parameter optimization, which comprises the following steps of: S1, constraining positioning of a follower by using a time sequence of an inertial trajectory of a leader, and constructing a collaborative positioning optimization objective function; s2, correcting an elevation error by utilizing elevation information, determining a solving target as an optimal rotation matrix and an optimal translation matrix, and introducing a spatial relative position constraint between a leader and a follower; s3, determining core elements of an optimal rotation matrix and an optimal translation matrix, discretizing the position space of the unmanned aerial vehicle into a translation grid, and defining a rotation search range; s4, constructing an objective function used for solving the optimal rotation matrix and the optimal translation matrix, designing a fixed step length iterative search method, performing traversal search to obtain the optimal rotation matrix and the optimal translation matrix, and performing calculation to obtain a correction position of the follower; according to the method, the anti-interference capability can be improved, high-precision positioning of the follower is realized, and meanwhile, the dimensionality and complexity of a resolving process space are reduced.

This invention discloses an automatic bone densitometer localization method based on multimodal sensing and AI contour matching. The method uses a multimodal sensing module to collect data on the patient's three-dimensional contour, body thickness distribution, respiratory micro-movements, and dynamic displacement. After preprocessing and fusion to generate a feature map, an improved U-Net++ model and ICP registration algorithm are used to achieve precise segmentation and registration of the bone contour. Hardware-based collaborative calibration, dynamic tracking compensation, and error correction are then employed to achieve automated, high-precision localization of the target bone. This method reduces reliance on patient cooperation, achieves a localization accuracy of ≤0.3mm, a process time of ≤10 seconds, and low radiation dose. It is suitable for special populations such as the elderly, children, and those with scoliosis, as well as complex clinical scenarios, significantly improving the efficiency, accuracy, and safety of bone density testing.

The application discloses an injection molding workpiece template matching method, an electronic device and a storage medium, and realizes a template matching mechanism by establishing a J(ψ ij ) arbitration function. The method comprises four main steps: injection molding workpiece image data preprocessing; contour feature extraction of an image data set and establishment of a standard template; obtaining matching accuracy by using a contour matching algorithm based on Hu moments on the injection molding workpiece and determining a matching threshold and a statistical scaling value thereof; and finally, using a plastic part template matching mechanism based on the J(ψ ij ) arbitration function to match the standard template with the workpiece to be tested. The application replaces the detection based on the gray value in the traditional detection method, so that the template matching of the injection molding workpiece has stability to factors such as translation, rotation and scaling. The application realizes automatic detection of defects of the injection molding workpiece, and the detection speed, accuracy and robustness are obviously improved compared with the traditional method.

This disclosure relates to the field of ore sorting technology, specifically to image recognition methods, material sorting methods, recognition devices, and sorting equipment. The image recognition method includes: acquiring a ray image of the material using a ray device and a color-sorted image of the material using a color-sorting camera; determining the ray contour of the material based on the ray image and the color-sorted contour of the material based on the color-sorted image; determining the motion state of the material relative to the conveying device based on the ray contour and the color-sorted contour; if the motion state is motion, then: determining a candidate contour in the color-sorted image corresponding to the ray contour based on the ray contour; and adjusting the candidate contour based on its position in the color-sorted image to obtain a first target contour of the material in the color-sorted image. This method can effectively improve the accuracy of contour matching between the ray image and the color-sorted image, thereby improving the accuracy of image recognition. In the material identification and sorting process, it improves the accuracy of material identification, thus improving the accuracy of sorting.

The invention discloses a seedling growth state image intelligent monitoring and quality grading method and system, and relates to the field of image monitoring. Synchronously acquiring a multi-view image sequence, an illumination parameter vector and a leaf surface reflection vector; identifying an overlapping region proportion and a contour point displacement vector of a seedling contour between adjacent multi-view image frames under different views, and constructing an overlapping difference atlas; performing cross-view-angle continuous tracking on the target seedling contour in the multi-view-angle image to generate a cross-view-angle structure image set; calculating a boundary stability mean value, a change standard deviation and a contour matching rate of the target seedling structure between adjacent visual angles, and generating a structure stability label; performing compensation processing on the boundary region outside the preset stable interval to generate a candidate structure boundary set of the single seedling; screening each candidate structure boundary to generate a verification structure image set; and extracting a morphological feature data set of the target seedling according to the verification structure image set, and generating a seedling quality grade. And the accuracy and the stability of growth state evaluation and quality grading results are improved.

The application discloses a visual real-time six-degree-of-freedom head pose estimation framework for a wearable optical pumped magnetometer-magnetoencephalography (OPM-MEG) system, aiming at solving the motion pose error problem caused by non-rigid head-helmet coupling. The framework includes two stages of initialization and real-time tracking: in the initialization stage, a hybrid differentiable rendering strategy is adopted, combined with soft rasterization and hard rendering technology, to realize high-precision initial pose alignment between the head and the helmet through multi-view contour matching; in the real-time tracking stage, based on the checkerboard marker, an improved PnP algorithm is used to estimate the real-time change of the head relative to the initial pose, and a multi-candidate solution screening and nonlinear optimization mechanism is introduced to suppress drift. The application does not need to rely on structured light or laser scanning hardware, can realize low-delay and high-robust real-time pose tracking, effectively reduces the registration error caused by motion, and improves the reliability and accuracy of the neural magnetic signal source positioning.

The invention discloses a UI screenshot contour matching method and device, and the method comprises the steps: obtaining a basic framework model, positioning at least one business scene specified by an automatic test request, and continuously collecting multiple frames of UI screenshots from each business scene; after the UI screenshots are preprocessed, candidate contour images are extracted to construct a training image set; performing structure fine tuning on the basic framework model and migrating an initial weight to generate a fine tuning model; performing iterative training on the fine tuning model by adopting the training image set to obtain a target matching model; after a to-be-matched UI screenshot is received, contour matching is conducted on the to-be-matched UI screenshot through the target matching model, and an initial contour diagram is generated; and performing post-processing optimization on the initial contour map to obtain a target contour map. Therefore, the image set is constructed in combination with preprocessing of dynamic and static UI screenshots, contour matching is performed through iterative training of the fine tuning model, core elements and background interference are effectively distinguished, and the positioning accuracy of the target contour is improved.

This application discloses a method and related apparatus for detecting lifting needles, relating to the field of semiconductor technology. The method includes: acquiring real-time infrared images of a lifting needle assembly; performing contour matching between the real-time infrared images of the lifting needle assembly and the infrared images of each sample in the infrared image set of the lifting needle assembly; wherein the infrared image set of the lifting needle assembly includes multiple sample infrared images of the lifting needle assembly under normal operating conditions; if no contour-matching image exists in the infrared image set of the lifting needle assembly, an abnormality warning command is generated, where the contour-matching image is the sample infrared image whose contour matches the real-time infrared image of the lifting needle assembly; the abnormality warning command is used to indicate that the lifting needle assembly is in an abnormal operating state. Therefore, this solution utilizes the strong correlation between the component shape and temperature distribution within a vacuum chamber, and identifies the abnormal state of the lifting needle assembly based on infrared imaging technology, improving the real-time performance and accuracy of lifting needle detection.

The utility model discloses a special device for detecting the outer diameter of an oil pipe joint, which is characterized in that a probe vertical rod is arranged at one end of a base reference, a probe cross rod is arranged on the probe vertical rod, a connector of the cross rod is inserted into the probe cross rod, a probe fixing block is sleeved at the other end of the connector of the cross rod, and a probe chuck is inserted into the probe fixing block; a dial micrometer is sleeved on the probe chuck, the shifting block is sleeved in the dial micrometer, and the probe head is screwed into the dial micrometer and locked; a front guide block and a rear guide block are respectively placed in an I-shaped groove of a bottom block, a groove is formed between the front guide block and the rear guide block, a limiting block is placed in the groove, a vertical plate is installed on the bottom block, a positioning block is installed on the vertical plate, a calibration block serves as a calibration reference to carry out fine adjustment on a probe before detection, and the calibration block is pushed between the front guide block and the rear guide block and is in contact with the limiting block; and the reference assembly is arranged on the base reference. The structural design is reasonable, part distribution is clear, and action is stable. Due to the fact that hardware contour matching is used, the self-centering function is achieved, and the operation performance is stable.