20 results about "Graph cut algorithm" patented technology

The invention discloses a complex slope runoff simulation method based on multi-source data fusion. Comprising the following steps: fusing an unmanned aerial vehicle LiDAR, a ground penetrating radar and a remote sensing image, and considering physical coupling relationships such as vegetation root systems to generate a high-precision and physically consistent initial earth surface parameter field; the method comprises the following steps: identifying micro-topographic depression through a graph cutting algorithm and the like, and constructing a dynamically evolved slope overflow network based on a depression filling-overflow mechanism; establishing an overflow-scour-infiltration dynamic feedback regulation and control mechanism, wherein the scour intensity calculated according to overflow data is used for correcting the saturated hydraulic conductivity of the soil in real time, and corrected parameters are immediately fed back to an improved Green-Ampt infiltration model; through coupling solution of a multi-time scale algorithm, spatial and temporal distribution of slope runoff is output, and early warning and inversion of critical rainfall conditions can be triggered. The underlying surface dynamic feedback process can be simulated, the simulation reliability is improved, and technical support is provided for mountain torrent early warning.

The invention provides a pipeline defect detection method, electronic equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: performing coordinate conversion on an initial fisheye video stream to obtain a multi-frame plane image; for any frame of plane image, determining a binarization mask of the frame of plane image based on a semantic segmentation network, and extracting feature points of a pipe wall region in the frame of plane image by taking the binarization mask as a constraint to form a feature point set; determining an optimal suture line between any two adjacent frames of feature point sets by adopting a graph cut algorithm based on energy minimization, and carrying out splicing to obtain a pipeline panorama; and identifying the pipeline panorama by using a deep neural network model to perform defect identification to obtain a target defect. According to the method, the binarization mask is used as a constraint, and non-pipe wall area interference is eliminated; an optimal suture line is determined and splicing is completed based on an energy minimization graph cut algorithm, so that inter-frame splicing gaps and artifacts are effectively eliminated, and rapid and accurate detection of target defects can be realized.

The invention discloses a circuit board welding spot detection method based on surface structured light, and the method comprises the steps: installing an electric rotatable polarizing film in front of a lens of a binocular camera, and synchronously obtaining stripe images at four orthogonal polarization angles in a structured light collection stage. A Fresnel equation and a micro-surface model are utilized to establish an analytic model of polarization state and phase error, pixel-by-pixel reflectivity suppression is carried out on original stripes, and active physical compensation of high reflection error is realized. A micro-area self-adaptive phase unwrapping algorithm based on welding spot topology priori is provided, a Markov random field with process priori constraint is constructed, a Graph Cuts algorithm is used for solving, and edge period dislocation is eliminated fundamentally. And a dynamic graph convolutional neural network is adopted to extract depth features of the welding spot cloud, and few-sample intelligent classification of each type of defects only needing 5-15 samples is realized through contrast quantity learning and a dynamic memory bank.

The application relates to a CAD vector data intelligent preprocessing method based on a multiple recognition mechanism, which comprises the following steps: parsing a DXF file to construct a CAD heterogeneous information graph; extracting text features for a layer node and generating a layer embedding vector through adaptive fusion of a gate unit by aggregating high-order semantic structure features through a meta path; introducing a dynamic threshold decision mechanism to generate a dynamic threshold according to global statistical features of a drawing to determine a background layer; constructing a global dependency graph containing cleaned dependent edges and semantic constraint edges, dividing the global dependency graph into weakly coupled connected subgraphs as independent optimization transactions by using a graph cut algorithm, and performing parallel execution to complete cross-dimension collaborative optimization; setting a multi-level logical checkpoint and generating an atomic operation log, and realizing tracing and accurate recovery based on an incremental storage mechanism. The application has the effects of significantly improving background layer recognition accuracy and scene adaptability, improving collaborative optimization efficiency, and guaranteeing operation safety and traceability.

The invention relates to the technical field of image processing, in particular to a large-range water area time-space dynamic change monitoring method fusing remote sensing and GIS, and the method comprises the steps: obtaining the hyperspectral remote sensing image data of a to-be-monitored water area in real time; aiming at each moment, constructing a feature vector of each pixel point and a center vector of each area label in the water area to be monitored by combining a normalized water index and a normalized vegetation index of each pixel point through gray difference and texture feature difference between each pixel point and an adjacent pixel point in the hyperspectral remote sensing image data and by combining the normalized water index and the normalized vegetation index of each pixel point; and constructing an energy function of a graph cut algorithm for segmenting the to-be-monitored water area, segmenting the to-be-monitored water area at each moment through the energy function, and carrying out spatial-temporal dynamic change monitoring on the to-be-monitored water area through segmentation results at different moments. The invention aims to improve the accuracy of monitoring the spatial-temporal dynamic change of the water area by improving the accuracy of area segmentation.

The application discloses a kind of multi-source ship target identification methods based on two-stage cooperative fusion, it is related to signal processing technical field, first with the optical camera on the plane and radar sensor acquisition target sea area data, including original optical image and radar image data, then using Gaussian filter to the data collected is preprocessed;Next, using deep learning algorithm is carried out sea-land segmentation, constructs loss function and optimizes network parameter to obtain segmentation mask, then according to the mask result removes land background area, obtains the optical and radar image after processing;Afterwards, using the registration method that improved SURF feature and graph cut algorithm are combined, by screening feature point pair and solving minimum value of energy function, optimal transformation parameter is obtained, image registration is realized;For the image after registration, based on adaptive fusion rule to fuse image;Finally, corresponding feature is extracted by hand feature extraction and based on deep learning feature extraction and is fused to obtain final target recognition result.

This invention discloses an intelligent analysis method for low-altitude remote sensing data from low-altitude unmanned aerial vehicle (UAV) hangars, belonging to the field of intelligent control technology. This scheme first collects multi-source heterogeneous data through timestamp alignment and hardware-triggered synchronization mechanisms; then, geometric correction is achieved through joint optimization of the RPC model and GCP, and image stitching is completed by combining SIFT feature matching and graph cut algorithms; subsequently, an improved YOLOv8 model is constructed to achieve multimodal fusion target detection, and semantically guided change detection is completed based on the Transformer algorithm; a high-precision 3D model is generated by fusing multi-source data using an enhanced NeRF algorithm, and vegetation analysis and trend prediction are completed using a dynamic weighting algorithm and an LSTM network; finally, a comprehensive report is generated based on a knowledge graph and interactive visualization output is provided. This invention effectively solves the problems of inefficient multi-source data processing and insufficient core detection accuracy in existing technologies, significantly improving data processing efficiency, detection accuracy, and analytical practicality, and is applicable to multiple scenarios such as ecological monitoring and resource exploration.

The application discloses a kind of narrow river detection methods suitable for spaceborne wide swath interferometric radar altimeter image, comprising: reading into spaceborne wide swath interferometric radar altimeter image;It is enhanced river linear feature by Gaussian difference preprocessing to suppress background;Curvature structure perception detector is constructed, curvature response map is calculated based on Hessian matrix eigenvalue, and feature fusion enhancement is carried out in conjunction with multi-direction structure consistency score;The binary image obtained by adaptive threshold segmentation based on local statistics is used as region label by carrying out adaptive threshold segmentation to enhanced feature map, maximum value is extracted in the region corresponding to enhanced feature map, seed point set is formed, and adaptive region growth based on queue priority is executed, to generate initial river center line;Markov random field model is introduced, global structure optimization and topological connectivity correction are carried out using graph cut algorithm, to obtain optimized river center line;Direction and radiation characteristic constraint is applied again, morphological dilation is carried out, and finally complete river mask is generated.

This invention relates to the field of smart fire protection technology, specifically to a method and system for dynamic assessment of building fire risks based on big data. It includes: S1, constructing a heterogeneous information network model, forming a heterogeneous information network graph of building fire protection based on multi-source data, and constructing a digital twin environment; S2, inputting the heterogeneous information network graph into a spatiotemporal graph convolutional network model, predicting the dynamic attribute weight changes of nodes and edges; S3, constructing a fire spread seepage model and a rescue failure seepage model based on evolutionary trends, and using a graph cut algorithm to identify critical seepage nodes / edges; S4, calculating fire resilience entropy based on the predicted network state; S5, extrapolating the runaway time window based on critical seepage nodes / edges and fire resilience entropy, generating risk assessment results, and mapping them to the digital twin environment for visualization. This invention can identify key weak points that lead to system collapse, predict the remaining time for fire spread to critical areas, and provide forward-looking decision support for fire rescue.

The invention relates to the technical field of remote sensing image processing, in particular to a cloud snow layer image segmentation method and system based on edge optimization and a storage medium, and the method comprises the steps: segmenting a foreground region containing a cloud layer and a snow layer on a remote sensing image through a ResNet network, and a background region; extracting the edge information of the cloud layer and the snow layer in the foreground region by using an edge detection operator to obtain the edge information of the foreground region; and fusing the edge information into a graph cut algorithm framework, and segmenting a cloud layer and a snow layer in the foreground region. According to the method, firstly, the ResNet stage is utilized to ensure that the cloud snow foreground is completely detected, then the edge optimization graph cutting stage is utilized to accurately guide the boundary by utilizing the mixed edge weight, so that the segmentation contour is highly matched with the real ground feature edge in the image, respective advantages of deep learning and the graph cutting model are fully utilized, and the image segmentation efficiency is improved. And high-precision and high-robustness automatic segmentation of the cloud layer and the snow layer in the remote sensing image is realized.

The invention relates to the technical field of water supply transformation, and discloses a water supply dispatching frequency conversion regulation and control strategy optimization method based on pressure balance, which comprises the following steps of: obtaining terrain elevation data and pipe network topology data of a construction area, performing data preprocessing and generating a terrain gradient feature vector; calculating gravitational potential energy distribution by using a terrain flow field coupling analysis algorithm, generating a terrain influence coefficient matrix, analyzing a pipe network segmentation condition caused by construction based on a graph cut algorithm, calculating topological change characteristics of an affected sub-network, and integrating the terrain influence coefficient matrix and the topological change characteristics. According to the method, a quantitative relation between gravitational potential energy distribution and pressure propagation is established through a terrain flow field coupling analysis algorithm, so that the influence of terrain factors on pressure can be accurately quantified, and pressure compensation requirements at different elevation positions are differentially processed by introducing a terrain influence coefficient matrix; the defect that the terrain influence is simplified into a single parameter by a traditional method is overcome.

The application provides an ultrasonic probe shell structure damage positioning method based on a multi-channel echo consistency atlas, and five-layer progressive signal preprocessing is sequentially performed on the data of each channel to screen effective envelope wave bands; apparent damage distances and Shannon entropy weighted integrated energy attributes of each wave band are calculated, a cross-channel distance consistency atlas is constructed based on distance values of all channels, and wave bands are clustered into a plurality of suspected damage distance communities through a graph cut algorithm; the reliability of each community is quantified from three dimensions of normalized community energy, pulse width distribution compactness and cross-channel appearance frequency, a self-adaptive weighted harmonic mean model is used for fusion output of community comprehensive confidence and identification of effective damage echoes; distance values of each wave band in an optimal confidence community are weighted and fused in a nonlinear square weight manner to obtain a final damage positioning distance. The method has sub-centimeter positioning accuracy, strong noise robustness and wide structural and working condition adaptability in the detection of defects of plate shell structures.

The application provides a blood vessel segmentation modeling method based on topological knowledge in medical images, first, a segmentation arteriovenous vessel model is created based on a U-Net neural network, and the model is trained by using sample images and their labeled results, in the training, a partial supervision method is used, and partial labeled data is added in the data set; a graph cut algorithm is used as post-processing to optimize the prediction result of the neural network, and the accuracy of the blood vessel segmentation result is improved; Grow Cuts, layer traversal and other algorithms are used to construct the topological structure of the blood vessel, including segmenting the blood vessel, obtaining the radius of the blood vessel and extracting the arteriovenous sub-tree structure. The application uses the mixed training of complete labels and incomplete labels in the data set, introduces the optimization algorithm such as graph cut, guarantees the segmentation accuracy, improves the robustness of the algorithm, realizes the construction of the topological structure of the blood vessel, and forms a complete algorithm process for extracting the blood vessel from the medical image and constructing the topological structure of the blood vessel.

The invention relates to the technical field of image processing, in particular to a rock debris three-dimensional CT image segmentation method, device and equipment based on an image segmentation algorithm and a storage medium. After a target rock debris three-dimensional image is obtained, a target image model is constructed according to the target rock debris three-dimensional image, and image segmentation processing is carried out on the target image model; obtaining an original image segmentation result; according to graph nodes and adjacent edges of the target graph model, performing quality evaluation on the original image segmentation result to obtain segmentation quality evaluation data; and performing optimization processing on the original image segmentation result according to the segmentation quality evaluation data to obtain a target image segmentation result. According to the invention, the method can automatically achieve the segmentation of the rock debris image, improves the automation degree of operation, reduces the time consumed by the segmentation of the rock debris image, and shortens the segmentation time, so that the segmentation of the rock debris image is more efficient. The rock debris image segmentation method is suitable for a processing scene in which a large number of rock debris three-dimensional images are segmented.

The invention discloses a narrow river detection method suitable for a satellite-borne wide-cradling interference radar altimeter image. The method comprises the following steps: reading in the satellite-borne wide-cradling interference radar altimeter image; gaussian difference preprocessing is carried out to suppress background and enhance the linear features of the river channel; constructing a curvature structure sensing detector, calculating a curvature response diagram based on a Hessian matrix eigenvalue, and performing feature fusion enhancement in combination with a multi-direction structure consistency score; performing self-adaptive threshold segmentation based on local statistics on the enhanced feature map to obtain a second map as a region mark, extracting a maximum value in a corresponding region of the enhanced feature map, forming a seed point set, executing self-adaptive region growth based on queue priority, and generating an initial river channel center line; introducing a Markov random field model, and performing global structure optimization and topological connectivity correction by using a graph cut algorithm to obtain an optimized river channel center line; and then applying direction and radiation characteristic constraints, performing morphological expansion, and generating a final complete riverway mask.

The application discloses a large-span arch bridge digital pre-assembly manufacturing control method, relates to the technical field of bridge engineering digital manufacturing, and takes the semantic importance of components, the distribution density of point clouds and the design connection relationship as cores, constructs semantic weight, distribution weight and connection relationship weight, eliminates overall deviation through weighted centroid calculation and decentralization, fuses the weight to construct a covariance matrix and solves an optimal rigid transformation through SVD decomposition, realizes global optimization in combination with an improved graph cut algorithm, and finally outputs aligned point clouds containing manufacturing errors. The public segment is taken as a benchmark to calibrate a reference line shape, data support is provided for pre-assembly target line shape calculation of conventional segments, the alignment priority of the core area is effectively strengthened, sparse point cloud and local deviation interference is avoided, the overall splicing line shape consistency of arch ribs is ensured, and accurate technical basis is provided for segment splicing construction.

The application provides an optimal splicing line acquisition method for super large file remote sensing images, comprising the following steps: obtaining two super large file remote sensing images with geographical coordinate reference; performing equal resolution down-sampling processing on the overlapping area of the two super large file remote sensing images, and establishing a directed graph for the down-sampled pixel points; applying a graph cut algorithm to find the minimum cut of the directed graph, and obtaining a rough splicing line; generating a buffer zone based on the rough splicing line; obtaining the strip-shaped overlapping area of the two images in the original super large file remote sensing images by mapping the buffer zone; establishing a directed graph for the pixel points in the strip-shaped overlapping area; and applying the graph cut algorithm to obtain the optimal splicing line of the strip-shaped overlapping area. The application reduces the number of pixel points to be traversed in the process of determining the splicing line by performing down-sampling on the super large file remote sensing images, so that the number of nodes and edges to be calculated in the graph cut algorithm is met, and the global optimal solution is ensured, thereby obtaining the optimal splicing line of the super large file remote sensing images.

The invention relates to the technical field of image data processing, in particular to a high-precision vectorization method of a grid electronic map, and solves the technical problem of poor vectorization effect of the grid electronic map in the prior art. The method comprises the following steps: acquiring a grid electronic map of a to-be-processed region; carrying out region segmentation on the grid electronic map based on a graph segmentation algorithm; wherein the shortest path of the graph cut algorithm is calculated by adopting an adaptive ant colony algorithm; and carrying out vectorization processing on the segmented grid electronic map.

The invention relates to an AI optimization method and device for space calculation, and the method comprises the steps: carrying out the three-dimensional scanning of a to-be-measured space environment based on a laser radar, and obtaining initial point cloud data; fusing multi-angle scanning information through a multi-modal enhancement and registration technology to generate a high-integrity point cloud data set; adopting a dynamic graph cut algorithm to realize accurate sub-region division of the irregular geometric structure; sub-region spatial features are extracted based on a transfer learning classifier, and initial virtual content is generated by fusing expert rule coding; and finally, dynamically adjusting the position, the size and the transition effect of the virtual object through geodesic distance interpolation and a real-time environment monitoring technology, and generating immersive optimized virtual content adaptive to the physical space. According to the method, the problems of point cloud data missing, irregular boundary segmentation distortion and insufficient dynamic collaboration of virtual and real contents in a complex scene can be solved, and the spatial modeling precision and interactive experience continuity of scenes such as cultural heritage protection and intelligent building planning can be improved.

A volumetric based three-dimensional (3D) object is segmented. A training dataset of 3D training objects is acquired. A neural network is defined by multiple layers including a linear layer and a graph layer. The linear layer operates on 2D objects and provides inputs to the graph layer that performs convolution operations on vertices of the 3D objects. A model, that approximates a shape diameter function (SDF) determines neighborhood diameters including a distance from a vertex of the 3D objects to an antipodal vertex. The model is generated by iterating through the 3D objects using the neural network to converge on weights of SDF features. An input mesh is acquired and the converged weights are used to approximate SDF values. The SDF values are input to a graph cut algorithm that generates vertex clusters defining a segmented part of the input mesh. The segmented part is visually displayed or provided.