45 results about "Boundary segment" patented technology

The invention relates to the technical field of computer graphic processing, in particular to an image generation method and system, which refines the boundary recovery and structure fusion of an image through a multi-stage processing path, improves the quality and detail presentation of the image, and identifies the image by identifying the change in a boundary section by means of the gray gradient information of boundary pixels. A visual path in a shielding area is accurately extracted, calculation of the visual path, a gray value and a direction vector and screening of jump amplitude help to determine boundary recovery nodes in an image, visual performance of an image shielding part is effectively enhanced, an interruption position in the image is accurately calibrated in the process, and the image shielding efficiency is improved. The method lays a foundation for subsequent texture splitting and structure fusion, successfully divides the texture region in the image through texture direction and phase change analysis, accurately tracks the boundary trajectory of texture splitting, can optimize the texture structure in the image through splitting boundary data, and further improves the overall quality of the image through a variation region distribution diagram. The method realizes efficient reconstruction and fusion of image details, not only improves the recovery quality of the image, but also enhances the detail perception ability of the image, and especially shows great advantages in shielding and complex image processing scenes.

The invention relates to the technical field of contour extraction, in particular to a knitted label edge contour extraction and optimization method, which comprises the following steps of: extracting an image edge gray scale trend and acquiring a fracture coordinate, extracting an edge track to generate a connection path, reconstructing an interference region connection path, converting rough edge region path distribution and screening out an abnormal path. According to the method, the gray abrupt change and edge interruption features in the image are identified, the fracture area is accurately positioned, structural integrity identification is enhanced, space correlation and direction fitting are carried out on edge endpoints in the interference area, the boundary recovery capability and the closure degree are improved, abnormal path segments are screened out through polar distribution, and therefore the detection accuracy is improved. Control nodes are set based on the position relation between the boundary segments and the overall chain, a boundary joint trend coordination structure is fused, the contour consistency and robustness under the complex background are improved, and the coherence recognition capability and extraction precision are enhanced.

The invention relates to the technical field of injection pump calibration, in particular to an injection pump automatic calibration method and system based on flow velocity stability, and the method comprises the following steps: S1, collecting flow velocity data in the operation process of an injection pump, and recognizing a boundary section of stable-to-unstable conversion based on a dynamic window; constructing a boundary drift indication map reflecting response delay, amplitude fluctuation and a rate inflection point; s2, identifying a consistency offset error mode from the atlas in combination with a multi-cycle drift behavior, marking a start window, a transfer window and a peak window, and generating a historical drift memory sequence; and S3, constructing a cross-cycle inheritance type calibration parameter template based on the memory sequence, and adaptively adjusting key parameters in input control logic to realize pre-calibration response before the injection pump is unstable. According to the method, by constructing the boundary drift map, the inheritance type calibration template and the dynamic parameter fusion mechanism, accurate pre-adjustment calibration and self-adaptive compensation optimization of the injection pump before the injection pump is in the unstable state are achieved.

The invention provides a vehicle driving method and system, terminal equipment and a storage medium. The method comprises the following steps: acquiring an obstacle boundary of a preset driving road section of a target vehicle; dividing the obstacle boundary based on a preset extremely narrow boundary threshold to obtain a plurality of road boundary segments; constructing a plurality of path objective functions based on a preset objective function and the vehicle boundary section data of each road section boundary section; constructing a constraint condition corresponding to the path objective function based on the data of each vehicle boundary segment; on the basis of the constraint condition, solving each path objective function based on a preset solver to obtain a plurality of vehicle track segments; and splicing the plurality of vehicle track segments to obtain a driving track, so that the target vehicle drives based on the driving track. According to the method, the reliability of optimizing the expected driving track can be improved, so that the vehicle can drive more safely based on the driving track.

The invention provides a thangka black and white structure line vectorization processing method and system based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: collecting a to-be-processed black and white structure line grid image; extracting skeletons and boundaries of corresponding lines of the black and white structure line raster image, and segmenting the skeletons and the boundaries to obtain a plurality of skeleton segments and a plurality of boundary segments; performing topology reconstruction based on the screened skeleton section and boundary section information to obtain a topological structure and a topological vector; performing alignment cutting and feature extraction on the black and white structure line grid image to output a drawing block and an embedded vector; and inputting the embedded vector, the topological vector and the state parameter into an RNN decoder to generate a stroke parameter corresponding to each time step, converting the stroke parameters into a local 2D raster image through differential rendering, and pasting the local 2D raster image back to the global canvas to obtain a vectorized raster image. According to the invention, the automation degree and precision of the black and white structure line grid image to vector representation can be improved.

The invention relates to the technical field of image processing, in particular to a computer image processing system based on deep learning, which determines a first contour boundary of a target object through a preprocessing module according to a deep learning model, and draws brightness expression curves of pixel points of each sub-region in different direction dimensions through a feature extraction module. Marking a non-confidence sub-region in the aggregation analysis region through a feature aggregation module, obtaining brightness expression curves of pixel points subjected to exposure adjustment in different direction dimensions through a local processing module so as to determine contour boundary fitting points, and fitting contour boundary sections of the non-confidence sub-region, and updating the first contour boundary through the output end and outputting a second contour boundary of the target object. According to the method, local region optimization is carried out aiming at insufficient local features in a target object contour boundary extraction process under a complex illumination condition, and high-precision extraction and dynamic updating of a target object contour in a complex environment are realized.

The present application relates to the field of natural resource investigation and geographic information processing, and discloses a multi-modal data fusion and ground object boundary identification processing method for field verification; current phase remote sensing images of a region to be verified, reference vector map patches, historical land attribute data, positioning trajectory data of a field verification terminal and a field photo collection correlation field template are acquired to construct a patch-level multi-modal basic data set; candidate ground object segmentation results and change area results are extracted to determine suspected problem patches; a boundary uncertainty band is generated for the boundary of the suspected problem patches and the to-be-verified boundary segments are divided; shooting guidance is triggered during the field verification process, field photos are collected, coarse registration and fine registration with local remote sensing images are completed; edge features of the field photos are back-projected to a map coordinate system to form a field confirmation boundary point set, the boundary of the suspected problem patches is corrected, verification confirmation patches are obtained, and patch-level verification evidence data is formed.

The invention discloses a three-dimensional point cloud road boundary extraction method based on point density significance analysis, and the method comprises the following specific steps: 1, extracting a ground supervoxel point cloud set in a point cloud scene through supervoxel segmentation and a supervoxel-based region growing method; step 2, performing preliminary boundary extraction on the ground super-voxel point cloud set, and extracting left and right rough boundary points through height difference and point density; step 3, on the basis of extracting the left and right rough boundary points, further eliminating false boundary points and a non-boundary high-density structure, and determining road boundary points through spatial segmentation and eight neighborhoods; and step 4, dividing a curved boundary section and a straight road boundary section according to the road boundary points to obtain a complete road boundary. According to the method, the boundary identification accuracy and continuity under the complex road in the urban scene can be improved.

The present invention discloses a comprehensive identification method for unsafe behaviors of construction site personnel, which specifically relates to the field of construction site image analysis and identification, including: constructing a boundary segment connection diagram through edge detection and Hough transform, and generating a risk mask area based on plane projection trajectory and buffer zone; performing instance segmentation and posture estimation on personnel, extracting joint points and calculating center of gravity points, screening key points according to operation type and constructing a relative vector sequence; extracting the angle between the main posture axis and the boundary normal of continuous frames, generating a direction consistency matrix and an outward-inclined behavior mark; finally, identifying the resident section within the risk mask area and outputting an oppressive illegal approach behavior label; realizing a "spatial oppression" judgment mechanism based on image feature recognition, ensuring early identification and risk warning of unsafe edge approach behaviors that exist when construction personnel perform different types of operations.

This invention discloses a blind zone detection method for handheld point cloud scanning based on the interaction between the acquisition area and the boundary, relating to the fields of 3D scanning and point cloud data processing technology. The method first acquires the device pose and local point cloud, preprocesses it, and then transfers it to a global horizontal coordinate system. A boundary feature extraction engine is used to identify room boundary lines and project them to form a 2D boundary contour map. Simultaneously, an effective acquisition loop is dynamically constructed and projected based on the effective measurement range parameters of the laser scanner and the current ambient lighting conditions. Boolean operations are performed on the effective acquisition loop and the boundary contour map to identify uncovered boundary segments as potential blind zones. The occlusion state is determined using the temporal information of continuous frame point clouds and a ray casting algorithm to identify the effective blind zone. This invention reduces the 3D integrity determination to 2D planar geometric intersection, eliminating the need for prior models and enabling real-time identification of dynamic occlusion and acquisition blind zones. This results in high computational efficiency and significantly improves the success rate and coverage integrity of handheld scanning operations.

A system includes one or more processors that obtain data from one or more sources. The sources include sources any of low resolution map data, historical data of an ego-vehicle position, and landmark data. The processors generate candidate segments indicative of potential lane boundaries, the candidate segments include any of a first candidate segment generated from the low resolution map data, a second candidate segment generated from the historical data of the ego-vehicle position, and a third candidate segment generated from the landmark data. The processors validate the generated candidate segments, align the validated and generated one or more candidate segments, and construct a segment based on the aligned, validated, and generated one or more candidate segments, the segment defining a lane boundary. The segment is used to compute a navigation path for the ego-vehicle to operate the ego-vehicle within the lane boundary defined by the segment.

This invention relates to the field of farmland remote sensing monitoring technology, and discloses a method for farmland boundary extraction using a combination of UAV remote sensing and lidar. The method first acquires a set of oblique UAV photographic images of the target area, airborne lidar point cloud data, and shooting attitude parameters to extract an initial visually recognized boundary line. Then, a discretized local polar coordinate occlusion perception model is constructed to screen boundary segments to be compensated. Subsequently, candidate canopy point clouds are extracted based on the boundary segments to be compensated, and the equivalent occlusion canopy height is calculated. A multi-view occlusion shrinkage compensation vector set is constructed to determine the visually corrected boundary points. Finally, lidar reference boundary points are extracted, and the farmland physical boundary line is reconstructed after dual-track consistency verification. This invention can effectively eliminate the systematic shrinkage deviation of the boundary caused by crop canopy occlusion under oblique perspectives, ensuring the boundary positioning accuracy in scenarios such as farmland rights confirmation.

The application relates to the field of oral digitization, and provides a method and system for constructing a lip area matching point pair for three-dimensional face homologization non-rigid registration. The method comprises the following steps: performing spatial pose alignment on a source three-dimensional face mesh model and a target three-dimensional face mesh model to obtain an initial alignment model pair; extracting a loop of a lip area boundary of a plurality of mesh models based on topological features to obtain a lip topological boundary loop; performing segmentation processing on the lip topological boundary loop to obtain a lip boundary segmentation set, and establishing a segmented corresponding relationship between the source three-dimensional face mesh model and the target three-dimensional face mesh model; adaptively sampling and quota distributing a plurality of lip boundary segments of the target three-dimensional face mesh model in the segmented corresponding relationship according to the arc length proportion of the plurality of segments in the lip boundary segmentation set, and performing proportional linear step sampling to obtain a lip area matching point pair with consistent logical indexes. The application improves the alignment accuracy of the lip boundary.

The present application relates to the technical field of image processing, and especially relates to a computer image processing system based on deep learning, which determines the first contour boundary of a target object according to a deep learning model through a preprocessing module, draws the brightness performance curves of the pixel points of each sub-region in different direction dimensions through a feature extraction module, marks the non-confidence sub-region in the aggregation analysis region through a feature aggregation module, obtains the brightness performance curves of the pixel points after adjusting the exposure in different direction dimensions through a local processing module to determine the contour boundary fitting points, fits the contour boundary section of the non-confidence sub-region, updates the first contour boundary through an output end and outputs the second contour boundary of the target object. The present application performs local region optimization for the local feature deficiency in the contour boundary extraction process of the target object under complex lighting conditions, and realizes high-precision extraction and dynamic updating of the target object contour in a complex environment.

This specification discloses several embodiments that provide a route planning method, apparatus, and computing device. The starting point and ending point of the navigation route are considered as the corresponding travel boundary points. The starting road segment matched by the starting point and the ending road segment matched by the ending point are considered as the travel boundary segments matched by the travel boundary points. Based on the first type of travel boundary segments that are close to the travel boundary points, a second type of travel boundary segments that may not be close enough to the travel boundary points but are still suitable as travel boundary segments are further identified.

The application belongs to the technical field of nuclear reactor physics numerical calculation, and discloses a symmetric boundary processing method and system of a neutron transport characteristic line method. A geometry core model is constructed through an MOC program, boundary conditions of the geometry core model are set, the geometry core model is arranged with characteristic lines to obtain characteristic line arrangement information, symmetry of the geometry core model is set, a corresponding additional module is generated, an equivalent complete module of the additional module is generated according to the symmetry based on the additional module, boundary segment angular flux of the equivalent complete module is calculated, and the equivalent complete module is calculated according to the characteristic line arrangement information and based on the boundary segment angular flux of the equivalent complete module and a known source term distribution to obtain the equivalent complete module. The application realizes reduction of the difficulty of characteristic line arrangement of the additional module, and further improves the efficiency of neutron transport calculation.

Systems and methods are provided for planning an agricultural operation for an agricultural machine in a working environment. This Includes determining a boundary for the working environment; determining a plurality of boundary segments for the boundary; and determining, for each of the boundary segments, a candidate operational path. A performance metric associated with the determined operational path for the boundary segment is used to select a primary boundary segment which is optimized for a given metric of the operation. Operational components associated with the agricultural machine can then be controlled based on the determined operational path, e.g. to guide the machine along the path or present the path to an operator.

The invention relates to the technical field of path control, in particular to a path planning method and system for an agricultural robot, and the method comprises the following steps: obtaining a path coordinate direction angle and recognizing a sudden change interval, marking boundary points to generate a correction reference set, and filing a path control record. According to the method, the agricultural robot path direction angle change is used as a continuous quantity to be sorted and output, the judgment basis of the steering change trend is established, the distribution sequence of the path jump interval is combined, the direction sudden change concentrated area is accurately recognized, the structure conversion node position is delimited, and the front-back direction change trend of the path is used as the basis. Removing non-transverse conversion nodes and extracting the non-transverse conversion nodes as boundary points, dynamically generating forward reference points according to the difference between the current position and the path direction when approaching a boundary section, and performing redirection angle conversion to realize path control attitude adjustment, and synchronizing the archiving path state and the adjustment behavior; and the precision of agricultural robot path identification, the flexibility of steering adjustment and the integrity of operation records are improved.

The present application relates to the technical field of contour extraction, in particular to a knitted label edge contour extraction and optimization method, comprising the following steps: extracting image edge gray trend and obtaining broken coordinates, extracting edge track to generate a connection path, reconstructing the connection path of the interference area, converting the path distribution of the burr area and screening out abnormal paths, and fusing the boundary structure to generate a label contour structure, in the present application, by identifying the gray mutation and edge interruption characteristics in the image, the broken area is accurately positioned, the structural integrity recognition is enhanced, the spatial correlation and direction fitting of the edge endpoints in the interference area are performed, the boundary recovery ability and closure degree are improved, the abnormal path segments are screened out through polar distribution, the burr influence is suppressed, the geometric stability is ensured, the control nodes are set based on the position relationship between the boundary segments and the overall chain, the trend coordination structure of the boundary connection is fused, the contour consistency and robustness in complex background are improved, and the coherent recognition ability and extraction accuracy are strengthened.

The invention discloses a method for determining an intersection area, which comprises the following steps of: determining the main direction of each branch based on the obtained course angle of the track of each branch of the intersection area and the first intersection point of the boundary of the intersection area, determining the track width sequence of each cross section line, and determining the course angle of the first intersection point of the boundary of the intersection area based on the slope of a curve formed by the track width sequence, and selecting a target trajectory width from the trajectory width sequence, determining a line segment corresponding to the target trajectory width as a boundary segment of each branch, generating a polygon according to the boundary segment of each branch, and re-determining the polygon as the intersection area. The invention also provides a device for determining the intersection area and the vehicle-mounted equipment.

This invention proposes a deep learning-based method and system for vectorizing the black and white structural lines of Thangka paintings, relating to the field of image processing technology. The method involves: acquiring a black and white structural line raster image; extracting the skeleton and boundaries of the corresponding lines from the raster image, and segmenting the skeleton and boundaries to obtain multiple skeleton segments and multiple boundary segments; performing topological reconstruction based on the selected skeleton and boundary segment information to obtain the topological structure and topological vectors; aligning, cropping, and extracting features from the black and white structural line raster image to output drawing blocks and embedding vectors; inputting the embedding vectors, topological vectors, and state parameters into an RNN decoder to generate stroke parameters for each time step; converting the stroke parameters into a local 2D raster image through differential rendering; and pasting the local 2D raster image back onto the global canvas to obtain a vectorized raster image. This application helps improve the automation and accuracy of converting black and white structural line raster images into vector representations.

The invention relates to the technical field of face recognition, in particular to a deep counterfeit face detection method based on fusion boundary features, and the method comprises the steps: specifying a light source requirement according to the skin color contrast in an initial image of a target recognition user; determining whether the specified light source execution requirement is qualified based on the facial brightness difference; determining an action direction of a specified action based on the face brightness difference; acquiring a frame-by-frame image when the target identification user makes a specified action so as to identify a hair and face junction area of each frame of image, and extracting a fusion boundary line; dividing into a plurality of boundary sections based on the fusion boundary line; acquiring corresponding fusion boundary coordination parameters based on the plurality of boundary segments to determine a plurality of high-risk boundary segments; calculating verification consistency characterization parameters based on the boundary time sequence and the light and shadow time sequence of the plurality of high-risk boundary segments; and determining a fake face risk value of the target recognition user based on the verification consistency characterization parameter and the high-risk boundary section proportion. According to the invention, the dynamic face counterfeiting detection precision is improved.

Embodiments of the present application relate to the technical field of machine control, and disclose a control method, system and device for a mobile robot. In the present application, in the process of tracking the boundary of a working area before mapping, after the initial boundary points of the boundary of the working area are extracted, the boundary points are subjected to optimization and adjustment processing easy for the mobile robot to track online, and the adjusted and optimized boundary points are used as the boundary points actually tracked by the mobile robot, so that in the process of boundary tracking, the mobile robot can only recognize the effective boundary section that can be tracked and walked through, approach the boundary section and accurately track the boundary section, thereby realizing tracking of the boundary of the working area.

The present invention relates to the field of data management technology, specifically a real-life three-dimensional data management system based on cloud computing, the system including a model verification module, a hierarchy generation module, a label collection module, a permission configuration module and a request sorting module. The present invention, through the calculation of connectivity based on boundary segments and the analysis of directional cosine similarity, can accurately identify problem points with closure defects or directional mutations in component boundaries, and build a consistency index set based on this, thereby improving the accuracy of model integrity judgment. In combination with facet structure parameters and weighted hierarchical evaluation methods, it can accurately delineate the level of regional detail levels, realize spatially targeted differential expression of detail level configuration, perform priority calculation based on the number of access requests and functional task matching results, improve component call efficiency and rendering response performance, and ensure service load optimization under multi-terminal access conditions and the rationality of cloud resource scheduling.

The present application relates to the technical field of topographic mapping, and particularly relates to a geological area mapping precision optimization method, which comprises the following steps: obtaining initial mapping data of a target geological area and extracting candidate boundary segments to form a candidate boundary belt; generating a conflict belt priority sequence according to a cross-scale stability index of the candidate boundary belt, a difference in neighborhood feature distribution on both sides and a misjudgment influence value; performing normal penetration re-measurement and tangential tracking re-measurement according to the conflict belt priority sequence, determining a boundary position and updating the candidate boundary belt; and performing constraint reconstruction according to the updated candidate boundary belt and outputting a mapping result. The present application can improve the accuracy of key boundary identification, enhance the re-measurement pertinence and maintain the real boundary in the reconstruction result.

The application discloses a CT image enhancement processing method for tumor boundary recognition, and relates to the technical field of medical image processing. The method comprises the following steps: S1, performing multi-scale decomposition on an original abdominal CT image, and obtaining a preprocessed coarse scale component and a preprocessed fine scale component through a multi-scale feature decoupling network; S2, fusing the preprocessed coarse scale component and the preprocessed fine scale component, obtaining a corresponding CT enhanced image, and obtaining an initial boundary contour of the CT enhanced image; and S3, according to the initial boundary contour, obtaining an uncertainty score corresponding to each sampling point, determining a corresponding low-confidence boundary segment, and performing decomposition and enhancement processing on the low-confidence boundary segment to obtain a final tumor boundary contour. The application can specifically correct unreliable parts in the boundary contour, significantly improves the clinical reliability and robustness of the final tumor boundary contour, and avoids overall boundary errors caused by local blurring or noise.

This application provides a method for determining cover positions, an occlusion method, corresponding devices, and electronic devices, relating to fields such as games, artificial intelligence, cloud technology, automatic navigation, and blockchain technology. The method includes: determining each boundary segment of a navigation grid within a predetermined range, and generating candidate query points on each boundary segment; performing ray detection on each candidate query point based on at least one direction to obtain the collision situation of each candidate query point; and determining the candidate query points with collision situations as query points, which are used to determine the position where virtual objects are occluded using cover in the virtual environment. This application embodiment can achieve accurate and efficient cover location, providing a basis for virtual characters to locate cover, thereby improving the accuracy of human-computer interaction in the virtual environment.

The present invention discloses a binary image boundary tracking method based on line-by-line scanning, which is applied to the field of image processing. Aiming at the problem that the existing image boundary tracking algorithms cannot simultaneously take into account the speed and accuracy of boundary tracking; firstly, the present invention marks the pixel points on the target contour; then, scans the binary image line by line from top to bottom in the order of pixel points from left to right in each row. When encountering a boundary contour point that has not been placed in any boundary segment, successively perform boundary connection judgment operations on the boundary contour points at positions P1, P2, P3, and P6 in its Moore neighborhood. Finally, after the line-by-line scanning is completed, the ordered boundary contour of the target is output.

The invention discloses a method and device for judging whether a target approaches a preset area in a geographic coordinate system, and the method comprises the steps: setting a boundary line of the preset area, decomposing the boundary line into a plurality of boundary line segments, and obtaining the geographic coordinates of two end points of each boundary line segment; determining the position relation between the target point and each boundary line segment based on the geographic coordinate distance from the target point to the two end points of the boundary line segment and the geographic coordinate distance between the two end points of the boundary line segment; selecting and calculating the shortest distance from the target point to each boundary line segment based on the position relation; determining the shortest distance from the target point to the boundary line according to the shortest distance from the target point to each boundary line segment, and judging whether the target point is close to or far away from a preset area according to the relationship between the shortest distance from the target point to the boundary line and a preset threshold value, according to the method, the shortest distance from the target point to the boundary line is accurately calculated in the geographic coordinate system to perform specific area approaching judgment, so that the accuracy of safety monitoring of the specific area can be effectively improved, and misjudgment is prevented.

This invention provides a method and system for dynamic generation and early warning of UAV electronic fences, relating to the field of electronic fence technology. The method includes: obtaining image acquisition quality parameters of the UAV based on feature points of the target area and images acquired by the UAV in real time; performing dynamic evaluation of sampling accuracy based on the task priority level of the electronic fence generation command and the image acquisition quality parameters of the UAV, and performing dynamic adjustment of sampling configuration based on the task requirement parameters and sensor performance parameters of the electronic fence generation command according to the dynamic evaluation results; performing dynamic adjustment of the update speed based on the relationship between the UAV flight speed and the update speed of each boundary segment according to the task priority level of the electronic fence generation command; and performing UAV electronic fence generation and early warning based on the dynamic adjustment results of the sampling configuration and the dynamic adjustment results of the boundary update speed, thus achieving adaptive dynamic matching between the electronic fence boundary update speed and the real-time flight status of the UAV.