42results about How to "Reduce false match rate" patented technology

The invention discloses a rapid scene matching and positioning method for an unmanned aerial vehicle in a large-range scene. The method comprises the following steps: making a data set, and training a semantic segmentation algorithm; generating image retrieval vectors and feature descriptors for different blocks by using a visual location recognition algorithm and a feature extraction algorithm, and making a base map database; in the flight process of the unmanned aerial vehicle, image data shot by the unmanned aerial vehicle in real time are sent to a semantic segmentation algorithm to generate a segmentation result image, a visual location recognition algorithm and a database are used for calculating similarity to obtain a coarse positioning area, and then features are extracted from the real-time image and are matched with a feature database to achieve fine matching. And finally obtaining geographic coordinates according to geographic mapping in the base map. The method effectively solves the problems that the unmanned aerial vehicle is difficult to quickly and initially position in a large scene range in a denial environment and is difficult to reposition after matching fails, and improves the reliability that the unmanned aerial vehicle can execute a scene matching task in various extremely challenging environments.

The invention provides a map positioning and constructing method based on laser SLAM (Simultaneous Localization and Mapping), the method proposes a point cloud registration algorithm fusing semantic and distribution information, the algorithm can utilize semantic label information of point cloud to optimize a correlation process of the point cloud, different down-sampling parameters can be distributed to the point cloud through the semantic label information, and the correlation process of the point cloud is optimized. Therefore, the information loss of the point cloud is reduced, and the probability of mismatching in registration is reduced. According to the method, optimization is carried out from the point cloud registration link of the SLAM method, and the positioning precision and the mapping quality of the SLAM method can be improved.

The invention provides a light spot matching method and electronic equipment. The accuracy and efficiency of light spot matching are improved. Comprising the following steps: performing pupil detection and light spot detection on a human eye image to obtain a position coordinate of a pupil center and a pixel position coordinate of each light spot; determining a to-be-matched LED lamp ring projection table corresponding to the position coordinates of the pupil centers according to the corresponding relation between the position coordinates of the pupil centers and the LED lamp ring projection tables; for any light spot, determining a light spot matching pair corresponding to the light spot based on an LED lamp matched with the light spot obtained by using the pixel position coordinates of the light spot and the to-be-matched LED lamp ring projection table; obtaining each curvature center according to each light spot matching pair; determining a first target curvature center by using the curvature centers obtained by screening the curvature centers; and for the light spot in any light spot matching pair, updating the light spot matching pair by using a target LED lamp which is matched with the light spot and is obtained based on the first target curvature center and the image position coordinate of the light spot.

This invention discloses a method and system for UI element localization in cross-platform, full-scenario RPA based on functional semantics, belonging to the field of robotic process automation technology. It includes a pre-construction step, an element recording step, and an element playback step. The pre-construction step involves pre-constructing a UI element functional semantic ontology library specific to the RPA business scenario. This ontology library includes a standardized element functional semantic tag system independent of the client side, business context rules corresponding to each semantic tag, and interactive behavior features. By pre-constructing a client-side independent functional semantic ontology library specific to the RPA business scenario, and using business functional semantics as the core, this invention constructs a cross-platform generalized feature vector for target elements, freeing them from the binding limitations of recording client features. After the same business process is recorded on the Web client, it can be directly run on PC desktop clients, mobile mini-programs, domestic terminals, and closed systems without interfaces without modification, effectively solving the problem of high repetitive development costs in cross-platform scenarios.

The invention provides a color feature extraction and correction method and system for drug sensitive plate MIC value interpretation and a computer readable storage medium. The method comprises the following steps: acquiring a to-be-processed drug sensitive plate image; performing visual preprocessing such as exposure, contrast and white balance on the image according to the configuration file; high-precision alignment correction of the image is realized by using ORB feature detection and a BFMatcher matching algorithm; generating a transparent mask with an Alpha channel based on predefined hole position contour data, and performing non-destructive target segmentation on the aligned image; and converting the target region to an HSV color space, extracting H-channel hue features, executing cyclic correction, eliminating the problem of numerical value jump of the red region, and obtaining stable color feature data. The method has the advantages of being high in image registration precision, accurate in segmentation, stable in color feature, high in robustness and the like, and automatic and standardized interpretation of the MIC value of the drug sensitive plate can be achieved under different shooting conditions.

The application discloses a kind of based on the task cooperation management method and system of offline application, it is related to offline data management technical field, the method includes: determining task cooperation type, and obtaining collaborative task resource package;Task cooperation system environment is generated based on collaborative task resource package;Receive collaborative data, the modification probability of collaborative data is predicted by the prediction model pre-trained in collaborative task resource package, and the collaborative data of high-risk prediction result is early warning and checking;According to the compression packing of collaborative data according to collaborative task type, and when restoring network connection, the packaged collaborative data is executed upload by priority scheduling mechanism.The fusion technical scheme of intelligent matching resource package, generating system environment with edge computing, pre-training model early warning and checking, priority scheduling upload effectively reduces resource package mismatch and parameter error rate, while reducing upload delay, improves offline cooperation efficiency and data reliability.

The invention discloses a video stabilization method based on inertial measurement unit data and feature point matching, and relates to the technical field of image processing, and the method comprises the following steps: estimating a compensation parameter according to IMU data, calculating a compensated image relative to a previous frame image based on the compensation parameter, and reselecting N feature points from the compensated image as reference feature points; according to the current frame of image and the last frame of compensated image, feature point matching is carried out in the IMU estimated feature point deviation range, whether the matched feature point pair is larger than N or not is judged, if yes, traversal fine tuning is carried out on the IMU data estimated compensation parameter, the fine tuning parameter corresponding to the optimal GSAD is selected to calculate the final transformation parameter, and the final transformation parameter is obtained. If not, using the IMU data estimation compensation parameter as a final transformation parameter; transforming the current frame image according to the final transformation parameter to obtain a compensated current frame image; the video stabilization method realizes stable video transmission.

The application belongs to the technical field of point cloud processing, and discloses a multi-modal semantic mapping method and system based on a master-slave architecture. The method comprises the following steps: S1, extracting feature points in point cloud data and calculating the covariance matrix of the neighborhood point cloud of each feature point; S2, solving the optimal pose corresponding to the minimum of the target function; S3, converting the obtained point cloud data into a world coordinate system using the optimal pose, fusing all frame point cloud data in the world coordinate system to form a three-dimensional point cloud map, projecting the three-dimensional point cloud map to a camera coordinate system, and inputting the three-dimensional point cloud map projected to the camera coordinate system into a semantic segmentation neural network to obtain the semantic label corresponding to each point cloud in the three-dimensional point cloud map, thereby achieving semantic mapping. Through the application, the problems of weak dynamic adaptability, insufficient model generalization ability and long-term running precision decay of the traditional three-dimensional semantic mapping method in a dynamic complex scene are solved.

The application relates to the technical field of building contour extraction, and discloses a building roof contour extraction method and device, electronic equipment and a storage medium, the method comprising the following steps: extracting a building boundary from an optical remote sensing image, and extracting a building vector frame from a vector map; performing global search in an initial search space by using a normalized cross-correlation similarity measure, performing template matching, and preliminarily positioning a building roof contour; calculating a global offset main direction angle, setting an angle tolerance, taking the global offset main direction angle as a central axis, taking the angle tolerance as a half width, generating a sector search space, performing local optimization in the sector search space by using the normalized cross-correlation similarity measure, performing template matching, iteratively adjusting the angle tolerance, converging the sector search space, repeatedly performing template matching, and obtaining a final building roof contour. The application carries out hierarchical matching from global search to local optimization, uses the normalized cross-correlation similarity measure template matching, and realizes building roof contour extraction.

This application relates to the field of image processing technology for fly traps, providing an adaptive stitching method and system for fly trap images. The method includes: extracting feature vectors from multiple images using the SIFT algorithm and a ResNet network respectively, and weighting and fusing the two features using adaptive weight coefficients 'a' generated by the ResNet network to obtain a fused feature vector; based on the fused feature vector, using an improved random sampling consensus algorithm to solve the homography matrix between the two images, performing non-uniform random sampling based on similarity during the sampling stage, and using the top K% of the most similar inliers to form a refined inlier set for calculation during the calculation stage; and completing image stitching based on the homography matrix. This application solves the technical problems of difficult feature matching, stitching misalignment, and obvious fusion traces caused by the small size, dense distribution, and high similarity of insect targets in fly trap images, thus improving the robustness, accuracy, and efficiency of image stitching.

The application discloses a wave image matching method based on fusion matching cost, and relates to the field of stereo matching, and comprises the following steps: S1, left and right eye images of waves are collected by using binocular cameras to determine a disparity range; S2, the left and right eye images of original waves collected in the step S1 are preprocessed; S3, a matching cost value of the wave images is obtained by using an improved Census algorithm; S4, improved Census cost, AD cost and gradient cost are fused to obtain a cost space; S5, a cross-domain is used for cost aggregation; S6, a winner-takes-all (WTA) strategy is used to calculate the disparity of the wave images; S7, the disparity map in the step S6 is subjected to left-right consistency checking, and is subjected to hole filling and sub-pixel optimization processing. The application adopts the above method, solves the problem that the disparity map obtained from the wave images is prone to a large number of invalid points and mismatching points, improves the correctness of wave stereo matching, and guarantees the matching precision of the depth discontinuous region.

The application provides a kind of robust laser SLAM repositioning and mapping method for embodied intelligent robot in the technical field of robot autonomous navigation, comprising: receiving original laser radar point cloud and IMU data, pre-processing point cloud to extract feature points;IMU pre-integration provides initial pose constraint;Loop closure detection is through the two-stage cascade of ScanContext global search and LinK3D local matching, obtains repositioning constraint;Front-end odometry matches feature points with local voxel map, combines IMU optimization to output pose estimation, filters key frame to generate laser odometry factor;With repositioning constraint as initial value, use improved ICP to accurately register to obtain repositioning factor;Finally, construct factor graph, jointly optimize IMU, laser odometry and repositioning factor, solve global consistent pose sequence and map.The application has the advantages that: it greatly improves the system robustness, repositioning accuracy and global map consistency of quadruped robot in severe motion and large-scale scene, while meeting the real-time constraint of embedded platform.

The invention provides a power distribution overhead line three-dimensional modeling method, system and device. The method comprises the steps that an unmanned aerial vehicle is used for collecting an urban area power distribution overhead line image according to a flight path; performing line pixel extraction on the image by using a dual-branch network of odd-even channel splitting to obtain a joint segmentation image; constructing a wire harness direction matching window in the local area based on the joint segmentation map, and generating a line three-dimensional point band through parallax calculation; and tracking the three-dimensional point belt of the line by adopting a progressive search strategy with a preferential maximum curvature radius, and generating a three-dimensional skeleton of the power distribution overhead line. The track of the invention can effectively avoid shielding in a complex urban area. The double-branch network can improve the recognition precision of a fine line target, and effectively isolates multi-directional interference textures such as communication lines and advertising boards. The wiring harness guided matching window fundamentally avoids the introduction of background noise, reduces the mismatching rate and improves the matching calculation efficiency. A smooth and continuous line skeleton can be generated through a progressive search strategy with the priority of the maximum curvature radius.

The present application provides a method and device for target positioning in a mobile scenario, which relates to the field of artificial intelligence and can also be used in the financial field, including: clustering the customer behavior images acquired during the business handling process according to the business environment to obtain a cluster image; matching the cluster image according to a predetermined template image to obtain a customer initial positioning image; removing mismatched points from the customer initial positioning image to obtain a customer target positioning image. The present application can cluster the customer behavior images acquired during the business handling process according to the business environment, and finally obtain a customer target positioning image.

The present application provides a positioning method, device and terminal device based on semantic information, which includes: semantically segmenting the acquired RGBD image to obtain the semantic label of each pixel; reconstructing the point cloud based on the RGBD image, and mapping the semantic label of each pixel to the reconstructed point cloud to obtain the semantic point cloud of the current frame with semantic labels and the semantic map of the three-dimensional scene; generating two-dimensional discrete semantic feature points for each three-dimensional semantic point cloud in the current frame and the semantic map, respectively, to obtain the corresponding two-dimensional semantic feature point image, and then performing three-dimensional semantic feature description on each feature point in the two-dimensional semantic feature point image; based on the three-dimensional semantic feature description, feature matching is performed on all feature points in the current frame and all feature points in the semantic map to obtain positioning information. By introducing semantic features, this method can effectively improve the feature matching efficiency during positioning and reduce the probability of mismatching.

The invention discloses an oral cavity three-dimensional scanning system and method based on dual-band heterogeneous structure light coding, and belongs to the technical field of optical three-dimensional scanning. The system comprises a light source, a coding light modulation device, at least two image acquisition units and a data processing unit, the light source can respectively emit first wave band light and second wave band light with different wave bands; the coding light modulator modulates the first wave band light into a main coding pattern containing discontinuous stripes and modulates the second wave band light into an auxiliary coding pattern containing continuous stripes, and the stripes of the two coding patterns are alternately arranged at intervals; the modulated first wavelength light and the modulated second wavelength light irradiate the surface of a measured area, and each image acquisition unit acquires a coded image reflected by the measured area; and the data processing unit decodes the main coding image to obtain a reference matching point pair and parallax data, decodes the auxiliary coding image based on the result to obtain a high-density extended matching point pair and extended parallax data, and completes integrated three-dimensional reconstruction through triangulation after integrating the two types of data.

The present invention discloses a feature extraction method, device and terminal suitable for infrared SLAM, the method comprising: obtaining an image to be processed, extracting line features of the image to be processed; determining feature points of the image to be processed according to the line features of the image to be processed; and determining the descriptor corresponding to each feature point according to the surrounding pixels of each feature point of the image to be processed. The present invention first extracts the line features of the image to be processed, and then determines the feature points and the descriptors of the feature points according to the line features. The line features are edge information in the image. In infrared images, the edge information of objects at different distances can be well preserved in the infrared images. Using line features for edge extraction can filter out the interference of nearby textures and reduce the false matching rate of infrared SLAM.

The embodiment of the invention provides a map data updating method and device, equipment, a storage medium and a program product, and particularly relates to the technical field of traffic information processing. The method comprises the following steps: acquiring map data of a target area, and acquiring geographic space data newly acquired in the target area; based on the geographic space data, extracting a first traffic facility set associated with a target road model in the map data; extracting a second traffic facility set from the map data based on the first traffic facility set; matching a first traffic facility in the first traffic facility set with a second traffic facility in the second traffic facility set to obtain a matching result; and according to a matching result, updating local traffic facility data of the target road model to obtain updated map data. The method is used for achieving the effect of improving the accuracy of updating the map data.

The invention discloses a pedestrian crossing traffic intention prediction method and a warning system at an aerial view angle, and belongs to the technical field of traffic management, and the prediction method comprises the following specific steps: S1, collecting and processing each group of data, and carrying out the cross-camera matching; s2, optimizing the matched comprehensive information graph and extracting feature data; s3, fusing the feature information of each group and predicting a pedestrian crossing traffic intention; the method is helpful to capture the uncertainty in pedestrian behaviors, can dynamically adapt to the change trend of the pedestrian behaviors, provides more elastic and intelligent intention prediction capability, enhances the adaptability in diversified scenes, reduces the probability of misclassification, can reduce the probability of mismatching, is helpful to reduce information loss and error accumulation, and improves the prediction efficiency. The robustness of cross-view information fusion is improved, the input data quality of overall pedestrian intention prediction is optimized, and a more stable pedestrian crossing intention prediction basis is provided.

The invention discloses a robot visual tracking method. The execution subject of the robot visual tracking method is a robot equipped with a camera and an inertial sensor. The robot visual tracking method comprises the following steps: the robot uses a window matching method to perform image tracking. When the robot successfully performs image tracking using the window matching method, the robot stops using the window matching method to perform image tracking, and then the robot uses a projection matching method to perform image tracking. Then, when the robot fails to perform image tracking using the projection matching method, the robot stops using the projection matching method to perform image tracking, and then the robot uses the window matching method to perform image tracking.

The application discloses a rule intelligent labeling generation method based on task semantic analysis, relates to the technical field of data labeling management, and specifically comprises the following steps: performing zero-sample rule cold start, comprehensively calculating confidence scores according to occurrence frequencies, semantic rationality scores and domain general prior knowledge, and selecting a seed rule set; acquiring the seed rule set, constructing a rule knowledge graph based on multi-dimensional semantic association relationships; acquiring a natural language sentence to be labeled, performing bidirectional refining on sentence semantics and rule semantics of the natural language sentence through a cross-attention mechanism, and calculating a matching score; on the rule knowledge graph, label information is propagated from the seed rule to a candidate rule through an attention mechanism; the propagated label and the matching label are compared, inconsistent rules are marked and preferentially submitted for verification; the rule knowledge graph is updated according to a verification result, and a final labeling rule set is iteratively generated.

The application discloses a dynamic target tracking and ranging method and system based on fusion matching, relates to the technical field of target tracking and identification, and specifically comprises the following steps: collecting video image data of a to-be-measured target and preprocessing; constructing a self-attention enhanced YOLOv8 target detection model to detect the to-be-measured target; predicting a next frame target estimation box of the video image data by using Kalman filtering, updating the target detection box, and positioning the to-be-measured target position; matching the predicted target estimation box with the current target detection box by using the Hungarian algorithm to establish the motion trajectory of the to-be-measured target; converting the coordinates of the to-be-measured target into coordinates in a robot world coordinate system according to the coordinates of the to-be-measured target in a tracking box sequence and based on the internal and external parameters of a camera; and calculating the distance of the to-be-measured target according to the coordinates in the robot world coordinate system. The application realizes accurate target positioning and ranging by converting the target from a pixel coordinate system to a world coordinate system, and solves the problem of inaccurate target distance calculation in a real-time dynamic environment.

The application discloses a complex background infrared image background motion compensation method based on image registration, which comprises the following steps: using a FAST feature point detection algorithm to detect feature points of two adjacent infrared images, and removing redundant feature points by using a quadtree algorithm; generating position descriptors and gray level descriptors, and combining the two with BEBLID descriptors to generate LGB descriptors for feature description of all preprocessed feature points in the two images; dividing each image into four quadrant regions, and calculating hash key values of each feature point; matching feature points in the two images according to the hash key values to obtain matched feature point pairs in the two images; screening the matched feature point pairs by using a random sample consensus algorithm, calculating final background motion parameters, and performing affine transformation on a previous image in the two images according to the final background motion parameters to realize background motion compensation. The application improves the effect of background motion compensation and provides better conditions for subsequent infrared dim and small target detection.

The invention relates to the technical field of information technology, and discloses a scientific research talent and enterprise information matching method and system, and the method comprises the steps: obtaining enterprise demand information, and constructing an enterprise demand feature set; obtaining scientific research achievement information, constructing an achievement feature set, and generating an application scene prediction result; obtaining enterprise historical technology information, constructing an enterprise technology layout feature set, and identifying a potential technology demand direction; obtaining scientific research talent information and constructing a scientific research talent ability feature set; mapping the feature set to a unified feature space to form an enterprise demand vector, an enterprise technology layout vector and a scientific research talent ability vector; and performing matching analysis on the scientific research talent capability vector, the enterprise demand vector and the enterprise technology layout vector based on the unified feature space to generate a matching result set, and outputting matched scientific research talent information and related scientific research achievement recommendation or cooperation docking information. The method can improve the industrialization efficiency of scientific achievements.

The invention discloses an information management method of an information management system, and the method comprises the following steps: determining an information type according to the existing database information, determining an information type label according to the information type, and finally dividing the information importance level according to the information type. The collected information is input, and the system automatically carries out information data preprocessing and quality verification; and after the quality verification is completed, the system automatically identifies the information type and classifies the information according to the known category label. According to the method, the mathematical model is established according to the known database, and the mathematical model is used for automatically classifying the information, so that a large amount of information data can be conveniently and quickly processed, and meanwhile, the method also has the functions of information data quality inspection, repeated data elimination and encryption of different levels of information, so that the information management is more efficient, accurate and safe.

The application relates to a SAR image robust registration method based on phase consistency and local scattering entropy, and belongs to the technical field of synthetic aperture radar image processing. The application comprises the following steps: firstly, pre-processing SAR complex data and calculating phase information, extracting stable feature points on the phase image by using the characteristic that the phase consistency is not sensitive to noise; then, calculating the local scattering entropy of each feature point, and constructing a feature descriptor which fuses phase distribution, scattering entropy value and spatial structure information; adopting bidirectional matching and scattering entropy similarity constraint to complete coarse registration and quickly correct large-scale deformation; taking the peak value of scattering entropy similarity in a small search window as a guide, and combining a nonlinear transformation model to realize sub-pixel level fine registration. The application can still stably extract features and realize high-precision matching under strong noise and complex scattering scenes, the registration precision can reach 0.08-0.1 pixels, and the application can be widely applied to the fields of SAR interferometry, ground surface change monitoring, disaster assessment and target positioning.

The application discloses a kind of light cross-view positioning method and system based on quantification knowledge distillation and semantic enhancement matching, comprising: unmanned aerial vehicle aerial image is used as query image, similarity matching is carried out between query image and pre-constructed geographic labeled satellite image database, and the highest similarity matching satellite image is obtained;Respectively extract the texture feature and semantic feature of query image and matching satellite image, and respectively generate corresponding semantic perception descriptor;Based on this, the texture feature similarity matrix and semantic feature similarity matrix between query image and matching satellite image are calculated respectively;Two similarity matrices are multiplied at element level, and the final similarity matrix is obtained, so that the pixel-level key point matching pair between query image and matching satellite image is determined;According to pixel-level key point matching pair and the geographic coordinates corresponding to matching satellite image, the geographic position coordinates of selected point in query image are obtained.The application realizes model light-weight deployment while ensuring high positioning accuracy.

The application discloses a river sudden environmental event early warning intelligent method and system, relates to the technical field of water environment monitoring, environmental emergency and intelligent early warning, and comprises a river network monitoring diagram of upstream and downstream monitoring nodes, risk source nodes and sensitive receptor nodes; online water quality, hydrological and risk source fingerprint data are acquired; upstream and downstream anomaly vectors are generated; a propagation time delay window and an attenuation coefficient are generated according to the length of a river section, flow velocity and flow rate; the upstream anomaly vector is translated in time and corrected in amplitude, and then the propagation consistency is calculated with the downstream anomaly vector; when the propagation consistency reaches a threshold value, only the candidate risk sources that are reachable by a hydrodynamic path are subjected to fingerprint matching, and a graded early warning is output in combination with the expected time window of a pollution group reaching a sensitive receptor and the influence level. The application can reduce single-point false positives, improve the accuracy of sudden water pollution event identification, source tracing and sensitive receptor early warning.

The invention provides a dynamic tracking method, system and device for an explosion fragment and a storage medium, and belongs to the field of optical tests.The method comprises the steps that a Bouguet epipolar correction algorithm is used for processing a binocular high-speed image, and matching search is reduced from two dimensions to one dimension; thirdly, providing a Rank-Census mixed binocular matching algorithm, and realizing high-precision matching and three-dimensional reconstruction of fragments with the same name in a complex environment by fusing the statistical robustness of Rank transformation and the texture sensitivity of Census transformation; secondly, a fragment diffusion physical model is introduced, the explosion center distance and the diffusion angle range of fragments are defined according to the charging structure, and a candidate space area of the fragments at the next moment is predicted; and finally, in the prediction area, adopting a speed consistency screening strategy, calculating an arithmetic mean value of the instantaneous speed of the candidate fragments, and selecting the fragments with the speed closest to the mean value for track association, thereby realizing stable and continuous tracking of the target fragments.

The present invention belongs to the technical field of computer vision three-dimensional reconstruction, and specifically is a three-dimensional reconstruction system and method based on panoramic binocular stereo vision. The three-dimensional reconstruction system of the present invention includes two panoramic cameras, two multi-dimensional adjustment frames and a computer processing system. The panoramic camera is composed of an image sensor and a quadratic catadioptric panoramic lens. According to the imaging relationship of the quadratic catadioptric panoramic camera, a plane-cylindrical projection model is established. The three-dimensional reconstruction method of the present invention includes: panoramic image acquisition and image processing to obtain a panoramic annular image; panoramic camera calibration; feature point detection of the panoramic annular image using a feature point extraction algorithm; feature matching using a balanced binary tree method; calculating the three-dimensional coordinates of actual object points to achieve panoramic three-dimensional reconstruction of sparse features. The present invention can quickly and efficiently achieve three-dimensional reconstruction of 360° annular scenes, and has broad application prospects in robot navigation, automatic driving, non-contact three-dimensional measurement and other aspects.