61 results about "Region growing" patented technology

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention discloses a binocular line laser reconstruction method and system, and relates to the technical field of computer vision, and the method comprises the following steps: S001, calibrating an internal reference and an external reference of a binocular camera, and obtaining a left image and a right image; arranging a line laser to respectively project parallel light knife surfaces on the left and right images; s002, respectively carrying out sub-pixel-level center extraction on the left image and the right image to obtain a binary mask, and calculating a candidate intersection point set; s003, calculating a basic matrix, determining a corresponding point on the right image based on the intersection point of the left image, and outputting a matching pair; s004, triangularizing the matching pair to obtain a 3D intersection point, and then marking the attributes of the light knife surface; s005, region growing is carried out after initialization; s006, calibrating a smooth knife surface equation; and S007, laser stripe full-pixel matching is carried out, and a final 3D point cloud is output. According to the invention, two or more groups of parallel line lasers in different directions are simultaneously utilized in the binocular system, so that unambiguous and high-density three-dimensional reconstruction is realized, the scanning efficiency is improved, and the ambiguity problem existing in the prior art is solved.

The invention discloses a large-parallax image splicing method and device, equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: performing feature point matching on a to-be-spliced feature point set and a reference feature point set to obtain a feature matching point pair set; according to the parallax value of each feature matching point pair in the feature matching point pair set, layering the parallax of an overlapping region intersected with the reference image in the to-be-spliced image to obtain a plurality of parallax layers and a matching feature point pair set corresponding to each parallax layer; according to the matching feature points to be spliced in the matching feature point pair set of each parallax layer, determining each geometric feature region by using a region growing method; and according to the global homography matrix and the local homography matrix of each geometric feature region, performing fusion processing on the to-be-spliced image and the reference image to obtain a target spliced image. By using the technical scheme provided by the invention, the accuracy of large parallax scene image splicing can be improved.

The present application relates to the technical field of medical image processing, and provides a knee femur and tibia segmentation method based on region growing, comprising the following steps: S1, acquiring knee medical images; S2, selecting a growth seed point; S3, coarsely segmenting the knee medical images to obtain knee stem data; S4, finely segmenting the knee medical images to obtain knee connection data; S5, fusing the knee stem data and the knee connection data to obtain knee femur and tibia image data with complete contours; S6, coarsely filling the knee femur and tibia image data to obtain knee femur and tibia image data after coarse filling; and S7, iteratively filling holes in the knee femur and tibia image data by using a three-dimensional sliding window, so as to obtain image data that is consistent with the surrounding data in shape, position and completeness and smoothness. The present application solves the problems of slow segmentation speed, unstable segmentation effect, incomplete segmentation and adhesion in preoperative planning of knee replacement surgery.

The application discloses a remote sensing image water body extraction method based on physical statistical distribution guidance and large model space constraint, comprising the following steps: acquiring a remote sensing image and performing pretreatment, calculating a physical statistical feature map and performing probability density distribution analysis, automatically determining a scene category to which the remote sensing image belongs, adaptively matching a hierarchical threshold calculation strategy, and acquiring a seed threshold and a growth threshold; performing region growing processing based on the seed threshold and the growth threshold to generate a physical prior coarse mask; performing connected domain analysis and adaptive outer expansion processing on the physical prior coarse mask to form a prompt box; inputting the pretreated remote sensing image and the prompt box into a visual basic model to output a fine segmentation prediction map; constructing a physical constraint search area based on the physical prior coarse mask, performing cascade fusion on the fine segmentation prediction map and the physical constraint search area, and obtaining a high-precision water body binary mask. Based on this, the application realizes automatic water body extraction without training samples, with strong noise resistance and fine edges.

A kind of photovoltaic panel foreign matter identification method and cleaning device suitable for micro-grid system, including the coordinate positioning of photovoltaic panel;Industrial camera collects the photovoltaic panel picture of positioning area;Photograph is identified using YOLOv3 algorithm and is identified again positioning;The focal length of industrial camera is adjusted using new positioning information to enlarge photovoltaic panel to the central part of industrial camera for picture capture;After the complete photovoltaic panel information is identified, the outline of processed photovoltaic panel picture is extracted, the area of photovoltaic panel is extracted using region growing method, and whether photovoltaic panel has foreign matter is judged;The positioning and identification problem of photovoltaic panel are realized by using the image recognition analysis technology of deep learning and convolutional neural network in the application, and the world coordinate system conversion method is used as the basis to enlarge and capture the corresponding position of photovoltaic panel by industrial camera, which has the advantage of accurate foreign matter identification, secondly, the cleaning device provided by the application has the advantages of simple structure, energy saving and high cleaning efficiency.

The invention discloses a real-time detection method and system for surface defects of an aluminum-plastic composite film based on machine vision, and particularly relates to the technical field of machine vision and image analysis. A high detection rate and a low false alarm rate cannot be considered due to the fact that a target signal is weak and is aliasing with noise; a sequence is formed by acquiring continuous multi-frame surface images; calculating the statistical magnitude of each pixel position in the time dimension; analyzing a collaborative change mode of the pixel points and spatial neighborhoods thereof based on the statistics to screen candidate points; performing region growth on the candidate points to form a connected abnormal region, and extracting the region feature quantity of the region; and finally, evaluating and marking by combining the time statistics and the spatial region characteristic quantity, thereby reliably identifying and positioning the micro-defects on the surface of the aluminum-plastic composite film from the noise background.

This invention relates to the field of tunnel detection and identification technology, specifically a method for detecting defects in water diversion tunnels based on a 3D model. The method includes: collecting laser point cloud data, panoramic image sequences, and ground-penetrating radar (GPR) profile data of the target water diversion tunnel section to form a multi-source detection data set. A 3D mesh model of the tunnel structure is reconstructed using the laser point cloud data. The panoramic image sequence is mapped onto the model to generate a textured 3D model. Abnormal signal segments from the GPR profile data are identified to obtain a set of radar anomaly depth markers, which are then projected onto the textured 3D model to construct a 3D detection model with defect markers. Candidate defect regions are extracted through region growing and segmentation. The opening width and extension length of each candidate defect region are quantified. Based on geometric features, the defect type is classified, and a defect detection report is output. This method achieves the fusion and correlation of multi-source detection data in 3D space, completing automatic defect region segmentation and geometric feature quantification.

The present application relates to the technical field of visual detection, and discloses a gear tooth profile defect detection method and system based on image recognition, the method comprising: carrying out pixel-level gray difference coding on a gear end face original image to obtain a gray coding sequence; based on the gray coding sequence, carrying out neighborhood comparison on the Hamming distance between pixels in the gear end face original image to obtain a local anomaly distribution; carrying out intensity sorting screening on the local anomaly distribution to obtain a candidate abnormal seed point; taking the candidate abnormal seed point as a starting point, carrying out similarity weighted region growing on the candidate abnormal seed point to obtain a suspected defect region; carrying out consistency checking on the suspected defect region, carrying out outlier elimination on the pixel coding deviation value of the checked region to obtain a refined defect region, and carrying out edge smoothing processing on the refined defect region to obtain a gear face damage region of the gear end face original image; the present application can improve the efficiency of a gear tooth profile defect detection based on image recognition.

The present application relates to the field of human state monitoring, and particularly relates to a human state monitoring method and system based on thermal imaging technology, the method comprising: acquiring an infrared thermal image and a visible light image of a human body; registering the infrared thermal image to the coordinate system of the visible light image and extracting a temperature field; performing posture evaluation on the visible light image to extract skeleton key points, and dividing the human body into multiple left-right symmetrical regions of interest based on the skeleton key points; calculating correction thermal symmetry difference values between the left-right symmetrical regions of interest to obtain a thermal symmetry difference map; performing region growing based on the point with the maximum pixel value in the thermal symmetry difference map to obtain an abnormal thermal spot region; calculating the product of an inflammation activity index and a boundary confidence of the abnormal thermal spot region to obtain a state risk value; and in response to the state risk value being greater than a set threshold, generating an alarm signal. The present application solves the problem of high false alarm rate of human state monitoring in a dynamic and complex environment.

This invention discloses a non-contact tire deformation recognition method based on a fine-tuned large-scale visual model. The method includes calibrating a monocular high-speed camera, fine-tuning the parameters of the mask decoder of the large-scale visual model using a tire image dataset, generating a point coordinate cue sequence based on pixel calculation using OpenCV, inputting the cue sequence into the fine-tuned large-scale model for segmentation, further processing the pixel matrix using OpenCV to generate a point cue sequence, and post-processing using iterative geometric fitting and region growing algorithms to obtain the mechanical deformation parameters of the target sample tire. This invention can achieve accurate and rapid tire deformation recognition, reaching pixel-level precise segmentation. It solves the problems of large errors, limited measurement environments, nonlinear image distortion caused by cameras, weak generalization ability, and high training costs in existing computer vision-based vehicle tire recognition methods.

The application provides a robot autonomous planning method based on a scene graph, and the method comprises the following steps: S1: reconstructing a three-dimensional point cloud of a scene based on multi-view images, and performing over-segmentation on the three-dimensional point cloud to obtain a plurality of point cloud segments; meanwhile, multi-view boundary maps are extracted from the multi-view images; S2: based on the multi-view boundary maps, the multi-view boundary affinity between any two adjacent point cloud segments is calculated; S3: based on the multi-view boundary affinity, region growing clustering is performed on all the point cloud segments to obtain object instances in the scene; S4: semantic information is given to each object instance, and a Voronoi diagram is constructed based on the spatial positions of all the object instances. The application utilizes the description of the spatial information and open semantic features of the object instances in the scene topology to assist a large language model in combining the task and behavior planning of the robot itself, and solves the problem that the task planner of the large language model is generally disconnected with the actual environment.

The invention discloses a welding path priority method and welding equipment for an automobile electronic ignition coil, and the welding method comprises the steps: obtaining point cloud data through preset track global scanning, generating a digital three-dimensional model consistent with a welding driving motion coordinate system through Gaussian filtering denoising, iterative nearest point registration, B-spline surface fitting and other strategies, and obtaining a welding driving motion coordinate system; the problem of coordinate deviation of traditional path planning is solved. A contour recognition strategy is combined with region growth, feature extraction and template comparison, and a least square method is used for fitting a center line to serve as a reference track; a node setting strategy is adopted, forced and uniform nodes are scientifically arranged, and accurate positioning of a welding head is guaranteed. A double correction mechanism of molten pool monitoring and node feedback is innovatively adopted, a thermal imager collects images and extracts molten pool parameters after preprocessing, a real-time correction instruction is generated in combination with three-level judgment, a lower node adjustment instruction is generated through image processing after node welding, closed-loop control is achieved, welding defects are reduced, and the percent of pass is increased. Corollary equipment adopts an integrated disc design and is matched with a rotary bearing mechanism to realize batch positioning and conveying, a multi-degree-of-freedom mechanical arm performs automatic welding according to a planned path, parameters are adjusted according to welding seam characteristics, and both precision and production efficiency are considered.

The invention belongs to the technical field of image processing, and particularly relates to a steel wheel weld joint welding quality detection method based on artificial intelligence. The method comprises the following steps: acquiring a steel wheel welding position image; carrying out weighted fusion Gaussian median hybrid filtering denoising on the image, positioning a weld boundary by combining Canny edge detection with Hough linear transformation, and extracting a weld ROI image by using a region growing method; constructing an ROI image gray matrix, straightening along the center line of the welding seam to form a gray sequence, and smoothing through moving average filtering; calculating a gray difference sequence, marking and merging abnormal segments through a sliding window, and extracting gray mean deviation degree and gray distribution entropy features; and normalizing the features, inputting the normalized features into a preset neural network model, and outputting a weld quality score. The detection accuracy and stability of the welding position of the steel wheel are improved, and the efficient quality control requirement of large-scale automatic production is met.

The invention relates to the technical field of fabric defect identification, in particular to a fabric defect identification method and system based on image processing, and the method comprises the steps: collecting a normal image of a jacquard knitted fabric, and processing the normal image to obtain a normal grayscale image; sampling the normal grayscale image to obtain a plurality of window images, and performing hierarchical clustering on the window images to obtain a plurality of clusters; training a convolution auto-encoder for each cluster, counting reconstruction errors of each pixel after all window images in each cluster are reconstructed by the convolution auto-encoder, and establishing a reconstruction error distribution reference library; the method comprises the following steps: acquiring a to-be-detected image of a to-be-detected jacquard knitted fabric, processing the to-be-detected image to obtain a to-be-detected window image, inputting the to-be-detected window image into a convolutional auto-encoder of a matched cluster to obtain a reconstruction error graph, and recognizing and extracting a defect area through a self-adaptive area growing method in combination with a reconstruction error distribution reference library of the cluster. According to the invention, the precision and robustness of defect detection are obviously improved.

This application relates to the field of image processing technology, specifically to a high-voltage power supply DNA sequencing visual inspection method and system. The method includes: acquiring a four-channel DNA electrophoresis image using Sanger sequencing technology; acquiring contours in the DNA electrophoresis grayscale image; constructing a DNA local density confidence coefficient and local DNA density parameters for each pixel based on the grayscale values ​​of each pixel within a channel and its neighboring pixels; acquiring the density run matrix for each pixel; constructing a DNA density distribution heterogeneity coefficient and channel tailing effect evaluation parameters for each pixel, and based on these, constructing a sequencing segmentation probability coefficient for each pixel; acquiring a decision threshold for each pixel; using the center of each contour as a seed point, and acquiring each DNA fragment based on an adaptive threshold using a region growing method, thereby obtaining the DNA sequence. This application can improve the accuracy of detecting the base sequence of a DNA sequence.

The embodiment of the invention provides a bone microstructure segmentation method and device, a storage medium and a program product. In the embodiment of the invention, a bone microstructure image is obtained, a weighted graph network is constructed according to the spatial change rate of voxel gray values, and the discrete curvature of each voxel block is calculated based on the weighted graph network so as to extract positive curvature components in a specified data range to form a positive curvature feature graph; and screening a target voxel block in combination with a bone tissue HU range, determining a maximum connected bone tissue region in the positive curvature feature map, and gradually complementing a boundary and an internal structure through region growth and pore filling so as to obtain a segmentation result of a bone microstructure. According to the method, curvature-driven geometric feature extraction and region growth evolution based on gray constraint are taken as the core, the segmentation problem in ultrahigh-resolution bone microstructure image processing is effectively solved, and automatic and high-precision segmentation of the bone microstructure image is realized on the premise of not depending on a large amount of manual annotation data.

The invention relates to the technical field of image data processing, in particular to a logistics violation behavior recognition method and device based on a monitoring video, and the method comprises the steps: obtaining an image in a logistics transportation process, extracting a plurality of shadow boundaries, carrying out the region growth through employing the midpoint of the shadow boundaries as a seed point, and generating an initial shadow; if the area ratio of the initial shadow to the shadow boundary is smaller than a set threshold value, removing the initial shadow to obtain a second shadow, and performing behavior recognition on the second shadow by adopting a behavior analysis algorithm; the shadow boundary extraction specifically comprises the steps of corroding a connected domain in an image foreground to obtain a first target, obtaining a second target through subtraction operation, and obtaining points on a unit circle by taking the geometric center of the second target as a circle center to form a feature vector; and mapping the feature vectors into a three-dimensional space for clustering, and calculating a mean value of target values in a cluster to obtain a shadow boundary. According to the invention, the problem of low logistics monitoring accuracy is solved.

The application relates to the technical field of product quality inspection, and provides an automatic product quality inspection method and system based on artificial intelligence vision, which comprises the following steps: acquiring an image sequence of a mobile screen through an overhead camera on a production line, extracting a screen contour, a motion direction vector and a moving speed, and establishing a motion reference; after frame-by-frame extraction of a screen image, adopting multi-scale segmentation combining region growing and edge constraint to obtain a to-be-judged connected domain with a centroid coordinate, an area and a gray feature; through comparison of interframe displacement of the to-be-judged element and screen reference displacement, and by using the characteristics that a real defect moves synchronously with a screen and a reflection does not follow the movement, false images are accurately removed, and defect candidates are screened; then, a defect enhancement image is generated through motion compensation alignment and superposition, and finally, a trained classifier is used to accurately classify defects such as scratches and concave points, so that the anti-interference ability and recognition accuracy of screen quality inspection are improved.

The invention relates to the technical field of machine vision image processing, and discloses a part surface defect detection method based on image processing. The method comprises the following steps: acquiring a part surface image through image acquisition, extracting defect sensitive characteristics by using a multi-scale Gaussian difference operator, and filtering through a threshold value to obtain a candidate defect point set; and inputting the candidate point set into a serialized network containing an attention mechanism and a memory unit to generate an enhanced feature sequence. And carrying out space alignment on the sequence and an original topological association graph, and generating an enhanced feature graph containing node attributes and edge weights through graph convolution. And on the basis, performing region growing and merging operation to generate a connected candidate defect region. And extracting geometric and texture descriptors of each region to form a feature vector, matching the feature vector with a standard vector in a defect feature library so as to identify the defect category and position, and finally outputting a defect detection map. According to the method, the detection rate and the segmentation precision of the complex distribution defects are improved.

The invention discloses a multi-method fusion laser radar atmospheric boundary layer height identification method, belongs to the field of laser radar atmospheric detection, and is used for solving the problem that residual layers, cloud layers and atmospheric boundary layer structures are difficult to distinguish. The method comprises the following steps: processing an atmospheric echo signal into a distance square correction echo signal data set, converting the distance square correction echo signal data set into a time-height two-dimensional matrix image, and processing the time-height two-dimensional matrix image to obtain a morphological edge detection original grayscale image; obtaining a preliminary boundary layer height through threshold segmentation, morphological operation and a region growing method, and differentially judging an edge line type in different time periods; fusing a gradient method and a wavelet covariance method to obtain final atmospheric boundary layer height estimation, final residual layer height estimation and final cloud layer height estimation; and processing all-day data to generate a time-height profile map containing height data and layer attributes. The atmospheric boundary layer, the cloud layer and the residual layer can be accurately distinguished, the wavelet scale is dynamically selected, the gradient method is fused, and the classification precision and the recognition stability of the atmospheric boundary layer, the cloud layer and the residual layer are improved.

The invention provides a tumor three-dimensional reconstruction method and system, and the method comprises the steps: carrying out the local gray feature analysis of a suspected tumor region of an ultrasonic image at each space angle, and carrying out the recognition of a stable highlight region formed by a tumor tissue in each ultrasonic image; all the stable highlight areas are projected to the same three-dimensional space, and then the intersection overlapping degree of all the stable highlight areas in the three-dimensional space is determined; performing spatial fusion on each stable highlight area according to the intersection overlapping degree, and generating a confidence structure point cloud of the tumor tissue based on a fusion result; and generating a three-dimensional entity model of the target tumor by taking the confidence structure point cloud of the tumor tissue as an initial starting point of region growth. By adopting the scheme of the invention, the model distortion of the three-dimensional model of the tumor area caused by noise can be avoided in combination with the spatial geometrical relationship of the multi-view image.

The invention discloses a method, a device, equipment and a medium for detecting a zero-value insulator in an insulator string, and relates to the technical field of fault detection, and the key points of the technical scheme are as follows: collecting an infrared image containing an insulator string in a to-be-detected power transmission line; inputting the infrared image into a pre-trained U-Net network for image segmentation, and outputting a segmented image of the insulator string; wherein the convolution operation of the U-Net network is sequentially completed by average pooling of a convolution kernel with the size of 1 * 1 and a spatial pyramid feature fusion module; segmenting an insulator steel cap region from the segmented image based on a region growing method; and extracting a steel cap temperature curve of the insulator steel cap region, and detecting a zero-value insulator in the insulator string in combination with a pre-configured threshold value of the relative temperature difference of the insulator steel caps. The problems that according to an existing image segmentation technology, the segmentation precision of a power transmission line insulator string is insufficient, the power transmission line insulator string is likely to be confused with other parts under the complex background, and the insulator string area cannot be accurately extracted are solved.

This invention relates to a multi-dimensional confidence-based point-line fusion SLAM method and system for visually degraded environments, belonging to the fields of computer vision and robot navigation technology. The invention constructs a nonlinear mapping function based on two-dimensional image entropy, dynamically adjusts the gradient threshold and Gaussian smoothing parameters of the line feature extraction algorithm, and introduces a sudden illumination safety mode to enhance adaptability to extreme environments. It performs photometric consistency verification on point features after optical flow tracking, performs region growing on line features using the aforementioned adaptive parameters, and proposes a hierarchical non-convex relaxation algorithm guided by dual weights of geometric residuals and pixel gradients for noise-resistant fitting. It establishes a linear inverse mapping mechanism between front-end feature confidence and back-end optimization information matrix, dynamically adjusting residual weights according to feature quality. In motion-blurred and illumination-changing environments, it can effectively extract high-quality features and perform tight-coupling optimization, improving the positioning accuracy and speed of the SLAM system in weak-texture and drastically changing illumination environments.

A method includes receiving image data including a plurality of CT scan images of a portion of a subject; identifying a seed feature within the plurality of CT scan images based on Hounsfield unit values within each image; applying region growing to iteratively generate a 3D model of the subject's colon starting with the seed feature; and applying a graph cut process to refine the 3D model to produce a finalized 3D model of the subject's colon.

This invention discloses a machine vision-based method for counting blocks of the same color, comprising the following steps: image acquisition and preprocessing; color space transformation and focusing to generate a color feature saliency map; generation of a two-dimensional spatial-color distribution vector set; seed point localization based on density peak search; object region division and final counting based on adaptive region growing; and output of the counting results. This invention completely eliminates the reliance on prior information about object shape and complete contours, utilizing color as a stable feature as the core clue. It effectively solves the problems of contact and occlusion between objects, accurately separating and counting even tightly clustered objects. It avoids complex and time-consuming model training processes, achieving a lightweight and highly robust solution based on traditional image processing and intelligent algorithms. It improves the overall robustness and adaptability of the system, enabling stable operation even under slight changes in lighting and relatively complex backgrounds, and is easily deployed on low-cost hardware platforms.

The application belongs to the technical field of image segmentation, and discloses a magnetic resonance head 3D image head shell segmentation method based on region growing threshold iteration, which comprises the following steps: acquiring a magnetic resonance head 3D image, resampling the magnetic resonance head 3D image to obtain a first image; performing head shell mask rough extraction based on the first image to obtain a second image, wherein each pixel of the second image is a binary mask; performing planarity extraction on the first image to obtain a third image, wherein each pixel of the third image is a scalar value; performing head shell mask fine extraction based on the second image and the third image to obtain a fourth image, wherein each pixel of the fourth image is a binary mask; performing post-processing on the fourth image to extract a head mask image in the fourth image; and performing pixel mask processing on the magnetic resonance head 3D image based on the head mask image to obtain a 3D head image with the head shell removed. The application eliminates the visual obstruction of voxels in the head shell region to the internal region, and reduces the difficulty of diagnosis.

This application provides an image segmentation method and system. The method involves performing basic preprocessing on the image to be processed to obtain initial image data with consistent structure; extracting spatial adjacency relationships and grayscale variation continuity between pixels from the initial image data, and constructing local structural feature regions of the image to be processed based on these relationships; performing region association analysis on all local structural feature regions, and determining region aggregation values ​​based on the comprehensive structural similarity between spatially adjacent local structural feature regions; determining a region association map reflecting the overall structural distribution of the image to be processed using the region aggregation values; and performing a segmentation process combining adaptive region growing and constrained splitting based on the region association map; and finally, performing boundary correction on each segmented region to obtain the segmentation result of the image to be processed. Using the scheme of this application, stable and controllable image segmentation can be achieved based on the region association mechanism of local structural features in the image.

This invention discloses an interactive 3D mesh segmentation and normal alignment completion method based on region growing, belonging to the fields of computer graphics and 3D model processing technology. The method includes the following steps: obtaining the numbers of multiple interactively selected triangular facets, extracting the spatial coordinates of the corresponding facets, calculating the cutting reference plane using a fitting algorithm, and aligning the plane center to the centroid coordinates of the selected facets through centroid correction. This invention achieves high-precision local segmentation through region growing and interactive guidance, significantly improving the segmentation accuracy and efficiency of 3D models and avoiding accidental cutting of adjacent structures. Simultaneously, it introduces facet completion and normal reordering mechanisms to solve problems such as non-closed models and inconsistent normals after cutting, generating a structurally continuous, watertight, and standardized 3D model that can be directly used for 3D printing, simulation, and visualization applications, possessing good industrial adaptability and engineering practical value.

The invention relates to a discontinuous microscopic image automatic splicing method based on multi-seed region growth and global layout, which comprises the following steps: acquiring a microscopic image sequence set to be spliced and preprocessing the microscopic image sequence set, processing the preprocessed image based on a multi-seed region growth method to form an associated forest structure, and carrying out local and global optimization on the associated forest structure, and generating and outputting a result. According to the scheme of the invention, an image set is intelligently divided into a plurality of connected sub-graphs (forest structures), local and global optimization is carried out on the connected sub-graphs, and global coordinate optimization of sub-graph nodes is carried out on adjacent nodes crossing the connected sub-graphs based on prior expected relative displacement. And carrying out splicing and coordinate optimization on the internal and global macroscopic layouts of each sub-graph, inhibiting error accumulation, and realizing high-precision splicing.