36 results about "Skeletonization" patented technology

The invention discloses a subway tunnel crack identification method and system based on deep learning, and relates to the field of tunnel structure health monitoring, and the method comprises the steps: carrying out the cylindrical expansion of an image of the inner wall of a tunnel, generating a mask through a shielding detection network, extracting a crack preliminary thermodynamic diagram, skeletonizing the crack preliminary thermodynamic diagram to obtain end points, and positioning a local candidate region at the paired end points, and a shielded crack path is recovered and a skeleton is extracted through combination of a geometric compensation network and topological continuity prior, and then the skeleton is fused with the initial thermodynamic diagram to output a frame-level geometric compensation crack diagram. Therefore, the problem of fracture structure interruption caused by shielding is effectively solved, and high-robustness fracture structure complete detection under interference of various shielding elements in a tunnel environment is realized.

The invention discloses an ultrasonic blood flow quantitative analysis method based on a skeletonized image, which comprises the following steps of: firstly, selecting a target analysis area containing a micro blood flow image in a grey-scale map formed by ultrasonic imaging; the method comprises the following steps of: firstly, carrying out normalization processing, nonlinear gray mapping, Gaussian filtering, minimum filtering and high-pass filtering in sequence to realize enhancement, noise suppression and edge enhancement on a target image, and finally, carrying out binaryzation on the image, and carrying out skeleton extraction based on an iterative refinement principle to obtain a final blood flow skeleton. According to the method, preprocessing such as noise suppression and edge enhancement is performed on the ultrasonic micro blood flow image before binarization and skeleton extraction, so that the accuracy and robustness of skeleton extraction are remarkably improved, and a reliable basis is provided for subsequent quantitative analysis and quantitative evaluation of a vascular network structure.

The invention mainly relates to the field of digital image photogrammetry. In order to solve the problems that road crack detection lacks end-to-end quantitative analysis capability and crack extraction results are fragmented, the invention provides a road crack extraction and grading method based on topological structure constraints, and the method comprises the following steps: crack end point extraction and width calculation, skeleton of a crack center line, identification of a connected region and end points, and classification of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability of the end-to-end quantitative analysis capability. The crack width corresponding to each center point is calculated through Euclidean distance transformation; potential breakpoint pairs are screened according to attribution, distance and direction constraints, then a topological connection rule is constructed, self-adaptive repair of the fracture is achieved, and the geometric integrity of the fracture is recovered; and performing quantitative analysis on the repaired cracks, accurately calculating the length, the average width, the maximum width, the area and the geographic coordinates of each crack, and performing danger grade division on each crack according to a preset threshold value to form structured disease data and a grading result which can be directly used for road risk assessment.

The invention discloses a real-time crack detection method and device based on coin reference, and the method comprises the steps: collecting a crack image containing a coin with a standard size through a smart phone, and achieving the precise quantitative measurement of the crack through coin detection, perspective transformation and crack analysis. According to the main technical scheme, 1) a coin detection module accurately identifies the position and contour of a reference coin through an improved ellipse detection algorithm in combination with a hand region shielding technology; 2) a perspective transformation module for realizing image geometric correction by using perspective transformation based on the coin position; and 3) a crack analysis module, which adopts a YOLO (You Only Look Once) algorithm and a skeletonization algorithm to realize submillimeter-level crack measurement. The invention provides a nondestructive testing scheme of combining the smart phone with the coin reference object for the first time, the hardware cost is remarkably reduced while the measurement precision is ensured, the detection precision can reach + / -0.2 mm, and the method is suitable for the fields of civil engineering detection and identification and the like.

This application provides a method and system for modeling multi-class lung airway tree models based on shape-aware sampling. The method includes: acquiring three-dimensional medical image data containing multiple types of tracheal anatomical annotations and determining a training set; determining sampling patches for the images in the training set based on a fractal dimension-driven sampling patch adjustment strategy; determining a skeleton-weighted loss term based on the training set using a shortest path cost skeletonization method; determining a total loss function based on the skeleton-weighted loss term and volume overlap loss; training a pre-defined neural network for multi-class lung airway tree modeling based on the sampling patches and total loss function of the images in the training set; and modeling the three-dimensional medical image of the lung to be modeled using the trained multi-class lung airway tree modeling model. This application effectively improves the volume overlap accuracy and topological consistency of multi-class tracheal model reconstruction, alleviating branch breakage and topological errors in discrete voxel space.

The present disclosure relates to a method for assessing the degree of damage to objects at disaster sites using skeletonization techniques, including the steps of moving a position of an investigation robot for information analysis of facility in a disaster site space to a first location by using a sensor module equipped in the investigation robot and including at least one of a LiDAR sensor, an IMU sensor or at least one vision sensor; acquiring sensing information corresponding to the first location based on SLAM; identifying a facility segment of a first space based on the sensing information; acquiring visual crack identification information corresponding to the facility segment, analyzed from vision image information of the sensing information, and unit crack information corresponding to the visual crack identification information; determining a crack expansion risk corresponding to the unit crack information.

The invention provides an antenna radiation characteristic obtaining method based on skeletonization and a MoM-PO algorithm. The method comprises the steps that an electrically-large-size platform and an antenna on the electrically-large-size platform are subdivided; defining an RWG primary function of the triangular patch pair; constructing matrix equations of the MoM region and the PO region based on the RWG primary functions of the MoM region and the PO region; the near-field matrix and the far-field matrix are filled based on an MoM-PO method and a skeletonization algorithm; and obtaining radiation characteristics of the antenna. According to the method, a skeletonization algorithm is adopted, far-field matrixes of coupling matrixes of the MoM and PO regions are filled through each group of impedance sub-matrixes of the coupling matrixes calculated through each group of interpolation matrixes of the MoM and PO regions and the coupling matrixes between the corresponding skeleton basis functions, the number of the skeleton basis functions is smaller than that of basis functions of the MoM and PO regions, and calculation is faster; the antenna radiation characteristic obtaining efficiency is effectively improved, and the computer memory requirement is reduced.

This invention discloses a method, apparatus, and medium for spatial coordinate unfolding and reconstruction, comprising: extracting spatial expression points labeled as target tissue types from spatial group data to generate a standardized binary image; performing a closing operation and smoothing edges, extracting multiple contour regions and merging them into a main contour, and performing an image skeletonization algorithm on the main contour to generate a skeleton path map; selecting the longest path from the shortest paths as the central main path; backprojecting the central main path in the image coordinates to the original spatial coordinate system to determine the central main path and its starting and ending points; constructing a tangent-normal coordinate system and calculating the distance from the spatial expression points to a first reference point; generating edge reference lines; projecting all spatial expression points to the unfolded coordinate system to determine the projection target; and projecting according to the projection target to obtain the unfolded coordinates. This invention automatically generates complete skeleton paths and highly smooth edge curves, effectively restoring the true spatial positions of the host and / or microorganisms and spatial expression points.

The method for three-dimensional branch reconstruction of tree point cloud based on mixed skeletonization and branch level simulation belongs to the field of forestry science research, computer vision technology and tree three-dimensional modeling technology. The method supports taking leafless single tree point cloud or leafy single tree point cloud as input, selecting the optimal weight file of the predicted point movement of the sparse convolutional neural network model trained on the leafless or leafy synthetic tree dataset, and applying it to the point cloud contraction process based on sparse convolution. The mixed skeletonization of the preprocessed input point cloud is obtained through the point cloud contraction and fast hierarchical clustering, and the initial skeleton of the single tree and the initial radius corresponding to each skeleton point are obtained. The two-stage skeleton refinement generates a single tree quantitative structure model, and the updated single tree quantitative structure model is obtained by adjusting the branch transition and reconstructing the branch arterial snake model. The model participates in parameter extraction together with the input point cloud and is attached to the single tree quantitative structure model file and output.

This application provides a medical image processing method and system. The method includes: acquiring vascular image volume data from photon-counting computed tomography scans of a target region; based on the vascular image volume data, performing skeletonization processing on the vascular network, extracting multiple skeleton points in the vascular network, and forming a vascular centerline network according to the connection relationships between the skeleton points; identifying endpoints and bifurcation points from the vascular centerline network, thereby dividing the vascular centerline network into several continuous and unbifuzzy vascular segments, and constructing a vascular centerline diagram structure based on each vascular segment; establishing and solving a blood flow analysis model based on the vascular centerline diagram structure to obtain the blood flow analysis results of the vascular network; wherein, the blood flow analysis results include the blood flow rate and / or pressure difference between the two ends of each vascular segment. This application improves the accuracy of the blood flow analysis results.

The application provides a mountainous area night animal tracking method and system based on multispectral and thermal imaging data fusion, and relates to the technical field of night animal tracking, and comprises the following steps: synchronously acquiring a multispectral image sequence and a thermal imaging image sequence in a mountainous area at night; performing multiscale Retinex enhancement processing on a registered near-infrared image and a thermal image respectively to obtain a strengthened candidate target image; extracting a multi-source texture signature of a fusion local binary pattern feature vector and a thermal gradient direction histogram for each candidate target, and combining geometric features obtained through morphological skeletonization to construct a multi-dimensional feature set; realizing target correlation between adjacent frames by using a nearest neighbor greedy matching algorithm to form a preliminary trajectory segment; and finally eliminating abnormal trajectories, solving trajectory intersection and occlusion conflicts through a sparse dynamic time warping algorithm, and generating an accurate and complete animal moving trajectory. The application can solve the conflicts caused by trajectory intersection and occlusion, and output continuous and accurate single animal moving trajectories.

The application discloses a skeleton quality driven tubular structure segmentation closed loop optimization method and system, which is applied to the technical field of biomedical image processing and computer aided diagnosis, and the method comprises the following steps: using a trained segmentation model to infer medical volume data to obtain a segmentation mask; skeletonizing the segmentation mask to extract a skeleton graph; performing topological defect detection on the skeleton graph to obtain a structured defect report; reversely mapping three-dimensional coordinates in the report back to a voxel space, taking each defect point as a center, a preset radius as a range, and generating a defect density weight graph according to a severity score; and using the weight graph as a spatial weighting parameter of a loss function to optimize the segmentation model; and the application reversely maps the topological defects detected by skeletonization into a weight graph and integrates the weight graph into a loss function for closed loop iteration, realizes directional repair on high-occurrence areas such as fractures and false branches, and improves the conduction efficiency of segmentation improvement to skeleton quality.

A method for generating a skeleton in an image of a cavity of an organ of a body includes receiving a map of the cavity, the map including surface voxels and interior voxels. A subset of the interior voxels is generated, of candidate locations to be on the skeleton. The subset is pruned by removing outlier candidate locations. Using a geometrical model including a statistical model, the candidate locations remaining in the pruned subset are spatially compressed. The compressed candidate locations are connected to produce one or more centerlines of the skeleton. At least the skeleton is displayed to user.

The application discloses a tunnel face joint information calculation method based on YOLOv8-seg, which comprises the following steps: a face joint data set is established, and data in the data set is labeled; the labeled joint data set is divided into a training set, a test set and a verification set, a YOLOv8-seg model is optimized, the joint data set is input into the optimized model for training, and a joint photo with a mask marked area is output after the training is completed; the joint information of the mask area is further processed according to the photo output by the model, the pixel height of the joint is calculated, and skeletonization is performed to extract to a single-pixel width; and finally, the actual height and length information of the joint are calculated according to the processing result of the previous step.

The embodiment of the specification provides a kind of recognition method and system of organism vessel, the method includes obtaining the three-dimensional image data of organism and its first segmentation result, second segmentation result, at least one organism vessel in second segmentation result is type pending vessel, fusion first segmentation result and second segmentation result, obtain fusion result;Skeletonization processing is carried out to first segmentation result, obtain first vessel skeleton set, wherein first vessel skeleton set includes at least one type determined vessel skeleton;Skeletonization processing is carried out to fusion result, obtain the vessel skeleton of type pending vessel;The type determined vessel skeleton in first vessel skeleton set is obtained as reference vessel skeleton set with the minimum spatial distance of the vessel skeleton of type pending vessel less than preset value D;Determine the nearest point group of the vessel skeleton of type pending vessel and reference vessel skeleton in reference vessel skeleton set;Determine the type of type pending vessel based on nearest point group.

The invention discloses a structure surface crack detection and quantitative analysis system based on deep learning, and relates to the crossing field of computer vision, deep learning and structure health monitoring. The system adopts a multi-stage cascade architecture. The method comprises the following steps: firstly, realizing crack intelligent identification through an improved OfficientNet-B0 model (integrating an ECA attention mechanism and an optimized MBConv structure); then, precise segmentation is completed based on a Mask-RCNN architecture; and finally, combining morphological processing and a camera calibration technology to realize size quantification. The core innovation points comprise: (1) a lightweight identification model of a double attention mechanism; (2) segmentation optimization of CLAHE enhancement and morphological refinement; and (3) a sub-millimeter quantization method based on skeletonization. Experimental results show that the recognition accuracy rate reaches 99.0%, the segmentation IoU is about 0.623, and the width quantization error lt is achieved; the thickness is 0.1 mm.

The invention relates to the technical field of medical informatization, and discloses a construction system for an orthopedic surgery analysis model, which comprises the following steps: acquiring original tomography data, and carrying out resampling and isotropic filtering processing on the original tomography data to obtain standard forearm three-dimensional image data; performing semantic recognition processing on the standard forearm three-dimensional image data to obtain a Boolean skeleton matrix; the positioning module is used for performing fracture surface positioning processing based on a Boolean skeleton matrix to extract fracture surface parameters; the skeleton processing module is used for performing skeletonization processing on the Boolean skeleton matrix to extract a radius center line and an ulna center line, and determining a functional rotation axis based on the radius center line and the ulna center line; and projecting the radius center line to the functional rotation axis to obtain a projection curve, and performing curvature analysis processing on the projection curve to obtain a radius arch top coordinate.

The application discloses a forearm shallow blood vessel segmentation method and device based on an NSVA-Net deep learning network, and relates to the technical field of medical image processing. The method comprises the following steps: collecting near-infrared images of the forearm shallow blood vessels of a subject by using a near-infrared light source and a near-infrared camera; performing denoising, contrast enhancement, cutting of the forearm region and normalization on the near-infrared images to obtain preprocessed images; performing two-stage image enhancement of RCAE and CLAHE on the preprocessed images to obtain enhanced images; inputting the enhanced images into a pre-trained NSVA-Net deep learning network to obtain a segmentation probability map of the forearm shallow blood vessels; performing threshold segmentation, connected domain analysis and skeletonization processing on the segmentation probability map to remove artifacts and isolated noise, and obtaining a blood vessel segmentation result and a center line; and superimposing and displaying the blood vessel segmentation result and the center line on the near-infrared images to output a final segmentation image. By using the application, the segmentation recall rate and the structural continuity of the forearm shallow small blood vessels of dialysis patients can be significantly improved.

The invention provides a self-adaptive palmprint multi-dimensional quantitative analysis method and system fused with anatomical features, two technical paths of anatomical guidance and deep learning segmentation are provided, accurate derivation can be performed by using medical priori knowledge, powerful identification can be realized by using a deep learning model, the two paths can be used independently or in combination, and the method and the system have good application prospects. The applicability of the technology is widened; key parameters of Canny and Hough algorithms are dynamically linked with image resolution and ROI (Region of Interest) size, so that the detection stability under images with different qualities and sizes is ensured; the palmprint ROI dynamically generated by the anatomy datum point is used for checking and restraining the output of the palmprint segmentation model, errors which possibly occur in the AI model and do not conform to the physiological common sense are effectively solved, and the recognition result is more reliable and accurate; through the techniques of skeletonization, topology analysis and the like, accurate quantification of features such as thickness, curvature, branch form and the like of palmprint which cannot be objectively measured in the past is realized, and unprecedented data dimensions are provided for scientific research of hand diagnosis.

The application relates to the technical field of brain science image processing, and discloses a brain tissue neuron morphology three-dimensional reconstruction and connection group analysis method and system. The method comprises the following steps: feature point extraction and matching and optical flow registration are performed on continuous ultrathin section electron microscope images to obtain three-dimensional image body data; a three-dimensional deep convolutional neural network is used for semantic segmentation of cell bodies, dendrites, axons and dendritic spines, and secondary registration is driven through confidence feedback; synapses are detected and classified into excitatory or inhibitory types; topological perception skeletonization reconstruction is performed, and segmentation correction is constrained through topological anomaly feedback; and a synapse connection matrix is constructed, and graph theory analysis is performed.

The application discloses a coal body hydraulic fracturing fracture three-dimensional space network reconstruction and characterization method, which is based on real CT three-dimensional body data, can realize the transformation of fracture structure from geometric visualization to parameterization and evaluation, can directly reflect the real connection relationship of the fracture in the three-dimensional space by identifying and three-dimensionally reconstructing the internal fracture of the coal body before and after fracturing, avoids the connection misjudgment and scale deviation caused by two-dimensional slice inference, improves the authenticity of the evaluation result from the source, and simultaneously adopts skeletonization and node-edge modeling on the fracture network, converts the three-dimensional voxel fracture structure into a node-edge form fracture network model, significantly reduces the calculation complexity under the premise of ensuring the fracture space connection relationship, enables the connectivity and flow conductivity characteristics of the fracture network to be expressed in the form of unified parameters, and is especially suitable for the fracture network characterization of the laboratory true triaxial test and the digital coal rock sample data based on CT scanning.

A method for providing a gripping point for mechanical interaction on an object includes (i) providing at least one image of the object, the image being a result of an acquisition by at least one sensor, (ii) providing object prompts based on the provided image by utilising an object detector and / or an unsupervised or semi-supervised segmentation method, (iii) performing a segmentation on the provided images using the provided object prompts to generate an object mask representing the object, (iv) performing a morphological skeletonization in the provided image based on the generated object mask to obtain a polyline, (v) reducing the polyline to a defined number of points, and (vi) selecting at least one of the points to provide the gripping point for mechanical interaction on the object. A computer program, an apparatus, and a storage medium for this purpose are also disclosed.

A method for generating a skeleton in an image of a cavity of an organ of a body includes receiving a map of the cavity, the map including surface voxels and interior voxels. A subset of the interior voxels is generated, of candidate locations to be on the skeleton. The subset is pruned by removing outlier candidate locations. Using a geometrical model including a statistical model, the candidate locations remaining in the pruned subset are spatially compressed. The compressed candidate locations are connected to produce one or more centerlines of the skeleton. At least the skeleton is displayed to user.

The invention discloses a crack feature quantification method and device based on camera calibration and skeletonization processing, and relates to the technical field of crack image recognition. The method comprises the following steps: performing image calibration according to a crack image based on a Zhang calibration method to obtain an internal and external parameter matrix and a distortion coefficient of a camera, a corner pixel coordinate of a two-dimensional grid calibration plate and a calibrated crack image; performing inverse perspective correction on the calibrated crack image based on the angular point world coordinates of the two-dimensional grid calibration plate according to the internal and external parameter matrixes, the distortion coefficient and the angular point pixel coordinates to obtain a corrected crack image; according to the corrected crack image, skeleton extraction of unit pixel width is carried out by using a skeletonization processing algorithm to obtain a crack skeleton image; and according to the crack skeleton image, performing calculation by using a feature quantization algorithm to obtain crack quantization features. The method is a non-contact, planar-coverage, low-cost and high-robustness crack feature quantification method.

This invention provides a method and apparatus for identifying cracks in ancient buildings based on image processing, belonging to the field of image processing. The method includes: acquiring a close-up image of cracks in an ancient building; segmenting the cracks using a region growing algorithm combining local Otsu's method and local mean; performing skeletonization processing on the segmented crack regions to obtain a crack skeleton with a single pixel width; and calculating the length and average width of the crack based on the area of ​​the crack region and the size of the crack skeleton, thereby achieving quantitative identification of the crack. This effectively reduces noise and background texture interference, improves the accuracy of seed point selection, and provides a more reliable and accurate technical means for detecting cracks in ancient buildings, helping to promptly identify potential safety issues in ancient buildings.

The invention provides an intelligent mirror multi-dimensional health assessment system, and relates to the technical field of health assessment, and the system comprises an acquisition module which is used for carrying out human body skeletonization processing on an acquired continuous three-dimensional image sequence of user motion and position information of user limbs in a space, extracting three-dimensional space coordinate data of key joints of a user body, and transmitting the three-dimensional space coordinate data to a processing module; obtaining a real-time skeleton model of user motion; and the pointing module is used for establishing a first reference vector pointing to the central points of the left and right shoulder joints and a second reference vector pointing to the central points of the left and right hip joints by taking the central point of the trunk of the user as a space base point based on the real-time skeleton model, so as to determine a main reference plane of the movement of the user. According to the method, through extraction of motion skeleton data, construction of a reference and dynamic feature plane, calculation and evaluation of key indexes, generation of motion attitude correction parameters and visual calibration guidance, evaluation and real-time optimization of motion quality are realized, the motion effect is improved, and the damage risk is reduced.

The invention provides a tunnel face joint fissure recognition method based on semi-supervised learning, and relates to the field of tunnel engineering.The tunnel face joint fissure recognition method comprises the steps that training images of tunnel face joint fissures are collected and marked, a first training set is constructed, and a second training set is constructed after the images are subjected to enhancement preprocessing; segmenting the second training set image by using a semantic segmentation model, obtaining a joint fissure space region, and performing multi-scale feature extraction; building a deep learning model introducing a semi-supervised learning framework, and training by using the feature map and the regional mask to obtain a prediction model; performing segmentation and feature extraction on a to-be-identified image, and inputting the image into the prediction model to obtain a region mask; skeletonization, connected domain analysis and multi-scale feature extraction are carried out based on the joint fissure mask prediction result, and the length and the inclination angle of the joint fissure are calculated to complete recognition.

The present invention relates to a method for evaluating the degree of damage to an object in a disaster site using a skeletonization technique, comprising: using a sensor module provided to a survey robot and having at least one of a laser radar sensor, an inertial measurement unit sensor, and one or more visual sensors; a step of moving the position of an investigation robot for performing information analysis on facilities in the disaster site space to a first position; acquiring sensing information corresponding to the first position on the basis of simultaneous localization and mapping; a step of identifying a facility segment of the first space based on the sensing information; acquiring visual crack identification information analyzed from the visual image information of the sensing information and unit crack information corresponding to the visual crack identification information in accordance with the facility segment; a step of determining a crack expansion risk level corresponding to the unit crack information; and constructing disaster site evaluation information of the first space by using the crack expansion risk degree and outputting the disaster site evaluation information to more than one set of equipment.

The invention discloses a space coordinate expansion and reconstruction method, equipment and medium, and the method comprises the steps: extracting a space expression point which is marked as a target tissue type from space group data, and generating a standardized binary image; executing closed operation and smoothing edges, extracting a plurality of contour areas and combining the contour areas into a main contour, and executing an image skeletonization algorithm on the main contour to generate a skeleton path diagram; selecting the longest path from the shortest paths as a central main path; back-projecting the central main path under the image coordinates to an original space coordinate system, and determining the central main path and a starting point and an ending point thereof; constructing a tangent-normal coordinate system, and calculating the distance from the spatial expression point to the first reference point; generating an edge reference line; projecting all spatial expression points to the expanded coordinate system, and determining a projection target; and performing projection according to the projection target to obtain expansion coordinates. According to the method, a complete skeleton path and a high-smoothness edge curve are automatically generated, and the real positions of hosts and / or microorganisms and spatial expression points in the space are effectively restored.

The invention relates to the technical field of industrial automation and machine vision, and provides a welding position recognition method and device based on point cloud projection skeletonization and a medium. According to the method, after three-dimensional point cloud of a workpiece is obtained, preprocessing and coordinate system alignment are carried out, vertical part point cloud is cut out and projected to a two-dimensional plane to generate an image; carrying out binarization and refinement to obtain a skeleton diagram; straight lines are detected from the skeleton drawing, a vertical welding seam is recognized according to the straight line included angle relation, and a horizontal welding seam is recognized by extracting an image contour; and finally, the identification result coordinates are inversely transformed to an original space. According to the method, three-dimensional recognition is converted into two-dimensional image processing, the robustness to point cloud missing is high, vertical and horizontal welding seams can be accurately recognized at the same time, the universality is good, and the calculation efficiency is high.