58 results about "Cut" patented technology

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention discloses a power system optimization method based on topology compression, and the method comprises the following steps: 1) constructing a power system topological graph: abstracting a power system into a weighted graph, representing power equipment by nodes, representing power transmission paths by edges, distributing electrical attributes and weights for the nodes and the edges, and providing a basis for subsequent topology compression and optimization; 2) analysis and implementation of topological compression: identifying redundant nodes and edges through cut point and bridge edge algorithms in a graph theory, and simplifying a topological structure by merging or removing redundant parts; 3) power flow analysis and optimal scheduling: performing power flow analysis on the compressed topological structure, and realizing power scheduling and load distribution by using an optimization algorithm to improve the operation efficiency of the system; and 4) dynamic adjustment and fault recovery: updating a topological structure and node attributes based on real-time data, and quickly responding to load fluctuation and equipment faults of the power system through an optimization algorithm. The method has the beneficial effects that the calculation complexity can be remarkably reduced, and the optimization effect and the scheduling efficiency are improved.

The invention relates to a distance perception-based graph division retrieval method and system and a readable storage medium. The method comprises the following steps of: constructing original data into an initial graph structure; distance measurement is selected, a weight is allocated to each edge, and the nearest neighbor node pairs are combined based on the weights to obtain a coarsened graph structure; performing hierarchical segmentation on the coarsened image structure, and projecting the coarsened image structure back to the initial image structure based on the segmentation condition to obtain a plurality of initial cut edge sets; finding a corresponding node according to each initial cut edge in the initial cut edge set, randomly putting the nodes into different partitions, and optimizing each initial cut edge set by taking the sum of minimum cut edge weights as a target function to obtain a graph index structure; and inputting a query, calling the graph index structure, respectively calculating the distance between the query and the central node of each partition of the graph index structure, and selecting the nearest partition for retrieval. According to the method, the number of times of cross-partition query is reduced, and the query efficiency is improved.

The invention provides a multi-target person image face replacement and skin color coordination method and system, and relates to the technical field of artificial intelligence image processing, and the method comprises the steps: receiving a source face image and a target image sequence containing a plurality of target persons; performing character detection on each target image, and retaining a character detection frame of which the confidence is higher than a threshold value; cutting the figure area based on the detection frame and expanding pixel redundancy to form an expanded cut image including a head and an adjacent skin area; zooming all the expanded and cut images to a uniform resolution ratio and then splicing the expanded and cut images into an optimized long image; and performing semantic segmentation on the optimized long graph, generating a multi-layer mask comprising a core mask layer, an extension mask layer and a coordination mask layer, and performing adaptive edge processing. Through a preprocessing strategy, multi-layer mask control, a style guiding mechanism and a skin color and illumination coordination technology, the quality, efficiency and practicability of a face changing result are improved.

The invention discloses a mask layout processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of mask layouts including cutting mask layouts and to-be-processed mask layouts, each cutting mask layout including a cutting pattern, and the to-be-processed mask layouts including isolated cutting patterns; adding an initial pseudo graph corresponding to the isolated cut graph in the mask layout to be processed based on the cut graph in the cut mask layout; predicting a simulation ADI result of the isolated cutting graph based on graph information of the isolated cutting graph in the to-be-processed mask layout added with the initial pseudo graph; when the simulation ADI result does not meet the preset condition, the initial pseudo graph is adjusted, and the graph information is updated based on the adjusted initial pseudo graph until the simulation ADI result meets the preset condition, and a target cutting mask layout corresponding to the to-be-processed mask layout is obtained. The problem that the process manufacturing requirement cannot be met due to the fact that the etching deviation of isolated cutting patterns is small is solved.

Implementations described herein relate to methods, systems, and computer-readable media to cut between coplanar convex polygons. In some implementations, a method includes obtaining the polygons, constructing a corresponding initial coplanar graph, adding that graph into a queue, and cutting each graph in the queue, the cutting including identifying whether the graph includes at least one concave boundary vertex. If there is no concave boundary vertex, the graph is added to a set of result graphs. If there is at least one concave boundary vertex, the cutting selects a starting vertex, finds an optimal cut from the starting vertex to another boundary vertex, splits the graph into subgraphs along the optimal cut, and queues the subgraphs for cutting. The cutting repeats until a termination criterion is satisfied. The method then outputs the result graphs. Using these result graphs may reduce the use of computing resources, such as in real-time applications.

The invention discloses a plate cutting method based on a 3D laser line scanning device, and the method comprises the following steps: arranging the 3D laser line scanning device above a to-be-cut plate, enabling the scanning visual field of the 3D laser line scanning device to cover the to-be-cut plate, and enabling the scanning visual field to cover the thickness of the to-be-cut plate; scanning the point cloud of the to-be-cut plate, then converting the point cloud into a height map, and then converting the height map into a grey-scale map; extracting edges of cut-off parts in the point cloud on the grey-scale map, independently establishing an X array and a Y array for each cut-off part, and storing the X array and the Y array into the arrays according to the edge sequence; the multiple arrays X and Y obtained in the third step are converted into dxf format files, the dxf format files are transmitted to a control system, graphic software is used for opening the dxf format files so that a user can determine an effective area by observing the collected height map, and further cut graphs are determined through the graphic software; and the control system stores the graph as coordinates and imports the coordinates into a cutting device for cutting.

The application discloses a kind of weak supervision learning-based hair defect image detection methods, the method includes the following steps: build detection device, obtain the image of the silk ingot product to be detected, using sliding window cuts out all possible hair image sample, using segmentation model to infer one by one to image sample, obtain sample segmentation graph;Sample segmentation graph is spliced, and the overall segmentation graph after processing integration is handled using morphological method;Hair block in overall segmentation graph and its area, bounding box information are counted;Integrate segmentation graph, the hair information existing on single silk ingot is counted, and the hair defect information of silk ingot is output.The application introduces weak supervision learning paradigm, overcome the performance bottleneck caused by the poor consistency of artificial label under supervised learning paradigm, improve the accuracy of silk ingot hair defect detection, while saving the cost required for manual annotation.

The application relates to a key power transmission section optimization identification method under an extreme event N-K broken line fault scene, which comprises the following steps: S1, converting a power grid into a non-directed graph, searching and deleting a hanging node in the non-directed graph, and finding a cut point of the simplified non-directed graph through a Tarjan algorithm; S2, performing power flow calculation on a large power grid to obtain power flow of each line in the power grid, and establishing a network flow graph of each partition of the large power grid according to a power flow calculation result and a maximum transmission power rating of the line; S3, iteratively searching by excluding each edge in an initial cut set in turn, identifying and deleting redundant cut edges and invalid cut edges, until the search of all minimum cut sets in the network flow graph is completed, and a power transmission section is obtained; and S4, obtaining a key power transmission section with an overload tripping risk when N-K lines are broken. The application can quickly and effectively identify the key power transmission section of the large power grid under an extreme event such as a natural disaster, and ensures safe and reliable operation of the large power grid.

The invention discloses a scale-controllable effective path set generation method, device and equipment, and relates to the field of path planning. The method comprises the following steps: creating a Dial rule family for screening effective paths; selecting a rule from the Dial rule family as a compression rule, screening road sections meeting the compression rule in the traffic network, and forming a sub-network; using a maximum flow algorithm in the graph theory to determine the minimum edge cut from the starting point to the end point in the sub-network; determining a preposed effective path set meeting a rule of being farther and farther away from the starting point from the starting point to a preposed node of the minimum edge cut; determining a post effective path set satisfying a rule of being closer to the end point between the effective path sets from the post node of the minimum edge cut to the end point; and combining the front effective path in the front effective path set, the minimum edge cut and the rear effective path in the rear effective path set to obtain an effective path set from the starting point to the ending point. The number of effective paths can be controlled.

The present disclosure provides a feature point matching method and device for images and a storage medium. The method comprises determining a plurality of candidate matching point pairs of feature points in two images, mapping the plurality of candidate matching point pairs into a network graph, establishing a first energy function representing the energy required for marking the plurality of candidate matching points in the network graph as correct matching point pairs or incorrect matching point pairs based on image disparity information, solving the minimum cut of the network graph as the optimal value of the first energy function by using a maximum flow / minimum cut algorithm, and determining the correct matching point pairs and the incorrect matching point pairs according to the minimum cut. The correct matching point pairs in the images can be determined by using the graph cut principle. This process does not need to rely on the setting of a threshold, thereby improving the accuracy of feature point matching. In addition, the present scheme does not need to perform model training and has strong scene generalization ability. Thus, the effect of feature point matching is improved.

The invention discloses an image segmentation method based on superpixel feature optimization graph segmentation. Comprising the following steps: firstly, acquiring and obtaining an original scar image through a camera, and then preprocessing the original scar image to obtain a preprocessed image; carrying out superpixel segmentation on the preprocessed image and extracting mean feature vectors corresponding to different superpixel areas; performing mask processing on the mean value feature vectors corresponding to all the superpixel regions to obtain rough segmentation masks; and finally, taking the rough segmentation mask as an initialization seed, and performing pixel-level edge refining on the original scar image by using an image segmentation method to obtain a refined segmentation mask. The method has the advantages of high segmentation precision, strong anti-noise capability, no need of manual seed marking and high automation degree, and can meet the actual application requirements of clinical diagnosis and quantitative analysis.

The present invention relates to a fast large-scale image data preprocessing method and application based on bipartite graph cut. Stretch and integrate n images of size a×b into an image data matrix #imgabs0#. Perform downsampling on the image data matrix X to obtain a bipartite graph matrix #imgabs1#. Orthogonally normalize F and G to obtain a discrete normalization model, and optimize the discrete normalization models of B, F, and G by alternately iteratively updating the constructed F and G. Obtain the prediction labels of n original image samples and m image anchors, and at this time the image data preprocessing process ends. After the preprocessing process is completed, large-scale out-of-sample new image data retrieval can be achieved by using full-sample clustering center comparison or image anchor comparison. This method not only avoids the singular value decomposition relaxation process and the discrete post-processing process, reduces information loss, but also greatly reduces the clustering time through the discrete coordinate ascent optimization algorithm, thereby realizing the high efficiency of large-scale data preprocessing.

The invention relates to the technical field of image processing, and particularly discloses a focus analysis method and device based on fundus images, and the method comprises the steps: obtaining a first fundus image set and a second fundus image set; performing image stitching on the first fundus image set and the second fundus image set to obtain a first stitched fundus image and a second stitched fundus image; performing intersection analysis on the second spliced eye fundus image based on the first spliced eye fundus image, and processing the first spliced eye fundus image and the second spliced eye fundus image based on an intersection analysis result to obtain a first intersection image and a second intersection image; cutting the first intersection image and the second intersection image to obtain a first cut image set and a second cut image set; performing focus analysis on the first cutting image set and the second cutting image set to generate first analysis data and second analysis data; and generating a lesion analysis result based on the first analysis data and the second analysis data. The accuracy and reliability of focus recognition are improved.

A processor-implemented method for implementing graph segmentation for interpretability using an artificial neural network (ANN) includes receiving an input via the ANN. The input is represented as a graph. The graph includes nodes connected by edges. The ANN determines a graph cut between a source node and a sink node associated with the input by solving a quadratic process with equality constraints. The ANN processes a subset of the input based on the graph segmentation to generate a prediction.

The present invention discloses a method, system, medium, and device for segmenting subscenes in long-strip drone images, relating to the field of digital photogrammetry. The method mainly comprises: constructing a scene graph using connection point constraints between images, simplifying it, and eliminating gross errors; analyzing the camera parameters of a single image to optimize the gradient direction by combining neighboring images that have a connection relationship with the current image; setting connection edge weights based on similarity metrics between image nodes, and achieving robust subscene segmentation using a normalized graph cut method. Implementing the method, system, medium, and device for segmenting subscenes in long-strip drone images provided by the present invention can improve the robustness of subscene segmentation in long-strip drone images and help improve the accuracy of subscene reconstruction.

The invention discloses a balanced clustering method for a cellular-removed large-scale MIMO (Multiple Input Multiple Output) system, which takes the spectrum efficiency maximization of the system as an optimization target, and establishes a spectrum efficiency maximization optimization problem P1 by clustering access points and users and constraining the access points and the users to only belong to one of the clusters; the system is converted into a bipartite graph according to the access point-user connection relation, and therefore the spectrum efficiency maximization optimization problem P1 is further converted into a bipartite graph division problem P2 with the minimum and maximum cutting target; the bipartite graph is converted into a traditional graph, a bipartite graph division problem P2 is further converted into a traditional graph segmentation problem P3 based on the traditional graph, an optimal clustering set is solved through a normalized spectral clustering algorithm, and in the solving process, the clustering number is adaptively determined based on a feature gap vector in the normalized spectral clustering algorithm. The method provided by the invention is more efficient in the aspect of interference control, and the spectrum efficiency of the system is remarkably improved.

Systems and methods are disclosed for identifying a set of internal edges on a representation of a network that satisfy a set of demands on the network. The disclosed systems and methods perform a multi-step process of selecting the internal edges. In a first step, an initial set of internal edges can be selected using a clique graph (or in another suitable manner). In a second step, a second set of internal edges can be selected using stream graph(s) (or in another suitable manner). The second set of internal edges can be used when determining network paths that satisfy the demands. When the representation of the network has a cut of two, the disclosed systems and methods can identify a set of internal edges providing a degree of protection against link failure.

The application discloses a kind of offshore area high-resolution three-dimensional reconstruction method and system, including using perspective size invariant feature to match the multi-view angle image of offshore area, according to the feature similarity guide sampling, obtain sparse feature point cloud;Based on structured local binary pattern, the depth of each pixel is obtained using graph cut method, and the sparse feature point cloud is encrypted to generate dense point cloud;Global error optimization is carried out to the point cloud of all view angles, and according to the depth information of the matching feature points, the three-dimensional coordinates of the feature points in space are generated;Strong and weak connection relationship between plane primitives is constructed, and the surface model is obtained by graph structure and energy function.The application can obtain wide area large range panoramic high-resolution information of sea area, construct three-dimensional model, and can intuitively display the space-time big data of offshore area.

The invention provides a river surface flow velocity identification data enhancement method and device, and belongs to the technical field of hydrological flow measurement, and the river surface flow velocity identification data enhancement method comprises the steps: obtaining a river data set; the flow velocity label is mapped to a gray value interval to obtain a gray value label graph, and the gray value of any point in the gray value label graph corresponds to the flow velocity; and randomly selecting a region of interest in each river original image and the gray scale label image, cutting the region of interest to obtain a cut image, and taking the cut image as training data of a deep learning model. According to the river surface flow velocity identification data enhancement method, the flow velocity label is mapped to the gray value, and the gray label image is made, so that compared with a traditional flow velocity label image or an optical flow image, the utilization efficiency of deep learning training data can be improved, and meanwhile, the utilization rate of the deep learning training data is improved. And different image receptive fields are provided by adopting a random cutting mode to improve the robustness of model training.

The present invention relates to an acceleration method for parallel calculation of maximum flow of large-scale graphs based on a cut point partitioning mechanism. The key steps include: 1) constructing a coverage map of the large-scale graph; 2) determining the unique path from the source point to the sink point on the coverage map; 3) submitting all subgraphs corresponding to the vertices on the path to the GraphChi platform for parallel maximum flow calculation and integration of the subgraph maximum flow. The present invention ensures the independence of the maximum flow calculation of each subgraph, reduces the number of communications, and integrates the results by parallel calculation of the maximum flow to ensure its computational efficiency and correctness. It can solve the maximum flow problem of large-scale graphs and significantly improve the calculation speed of solving the maximum flow. Experimental results show that this method can make full use of the characteristics of the problem itself to cope with the scenario of calculating the maximum flow of large-scale graphs. Compared with classical algorithms, it can effectively accelerate the calculation speed of the maximum flow.

The invention discloses a phase unwrapping method based on a space-time diagram cut algorithm, and the method comprises the following steps: 1, extracting a wrapped phase according to a result obtained through the analysis based on the fringe pattern analysis of structured light fringe projection; 2, constructing a space-time diagram based on the wrapped phase in the step 1; according to the constructed space-time diagram, defining an energy function, and using a space-time diagram cut algorithm to minimize energy; and step 3, performing phase unwrapping according to a result obtained by graph segmentation, constructing a continuous unwrapped phase graph, and completing phase unwrapping. The phase unwrapping method can adapt to complex and dynamically changing scenes, and the phase unwrapping accuracy is improved.

To provide a structural member provided with a protective film having excellent particle resistance. [Solution] A structural member (10) comprises a substrate (100) and a protective film (200) covering a surface (S1) of the substrate (100). When a cross section of the substrate (100) cut perpendicularly to its surface (S1) is taken as a cut surface, the shape of the line on the cut surface corresponding to the surface (S1) of the substrate (100) is taken as a surface shape, a function representing the surface shape is subjected to a Fourier transform, and the transformed function is plotted as a graph with wavelength (μm) as the horizontal axis, the average slope of the graph in the wavelength range of 30 μm or less is 0.0050 or less.

The invention provides a metal pipe mold-free bending method and system based on three-dimensional expansion and segmented V-shaped notches. The bending method comprises the following steps that S1, an expansion graph containing material compensation parameters is generated through three-dimensional modeling; s2, a progressive V-shaped notch set is machined in the surface of the pipe according to the unfolded graph; s3, a segmented positioning device is adopted for conducting segmented bending on the pipe provided with the V-shaped notch set so as to complete angle forming; the bending system comprises a modeling module, an unfolding module, a processing module and a bending module which are in signal connection in sequence. Force is applied section by section through the section positioning device, a special mold is not needed, the mold dependence problem is directly solved, and small-batch production does not need to bear the mold cost; the expanded graph is matched with the geometrical characteristics of the to-be-bent area of the pipe, material compensation parameters are combined with the physical characteristics of the pipe and bending process parameters, the size deviation is reduced from the source, and a foundation is laid for follow-up accurate bending.

A method and high-performance apparatus for creating an approximate maximum k-cut of a graph. In various embodiments, nodes and weighted edges in a graph are used to compute a partitioning of a graph such that the edge weight between partitions is maximized. In various embodiments, the method and apparatus use a greedy random construction of cuts for its first approximation, whose solutions are then subject to local search for better solutions, and local path-relinking for the final level of refinement. In various embodiments, one or more of the greedy random construction, local search, and path relinking are parallelized. A novel parallel algorithm is disclosed for perturbing the state of a cut to see if a better one is cheaply available.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide self-supervised object discovery systems that combine motion and appearance information to generate segmentation masks from a digital image or digital video and delineate one or more salient objects within the digital image / digital video. The disclosed systems utilize a neural network encoder to generate a fully connected graph based on image patches from the digital input, incorporating image patch feature and optical flow patch feature similarities to produce edge weights. The disclosed systems partition the generated graph to produce a segmentation mask. Furthermore, the disclosed systems iteratively train a segmentation network based on the segmentation mask as a pseudo-ground truth via a bootstrapped, self-training process. By utilizing both motion and appearance information to generate a bi-partitioned graph, the disclosed systems produce high-quality object segmentation masks that represent a foreground and background of digital inputs.

The invention discloses a multi-target merging method in a cross-camera scene based on effective cut points, a medium and equipment, and relates to the technical field of image analysis. Through hierarchical clustering combination, particularly similar targets under different cameras are combined into one cluster; according to the method, the feature similarity between clusters is utilized to carry out threshold constraint and merge into an undirected graph, and a graph analysis method is adopted to segment the undirected graph through effective cut points, so that the false merging phenomenon caused by jump noise is reduced, and the accuracy of multi-target merging in a cross-camera scene is improved.

The present disclosure relates to the field of communication technology, and to a method and device for establishing a separation path. The method includes: constructing an initial graph based on source nodes and destination nodes in a communication network topology, and adding the initial graph to a to-be-verified graph set; performing an iterative process until the to-be-verified graph set is empty: obtaining a current graph from the to-be-verified graph set; if a first path of a source node and a destination node is determined based on the current graph and a second path is determined based on the remaining nodes in the initial graph except for the nodes passed through by the first path, forming a current path group based on the first path and the second path to update the optimal path group, and returning to the step of obtaining the current graph from the to-be-verified graph set; if the second path cannot be determined, obtaining a cut point set of the first path, cropping the current graph based on the nodes in the cut point set, adding the cropped subgraphs to the to-be-verified graph set, and returning to the step of obtaining the current graph; determining two separation paths based on the optimal path group corresponding to the i-th iterative process, where i is the total number of iterations.

The invention provides an image retrieval method and system based on a large model. The method comprises the following steps: extracting text information of an original picture; converting the original picture into a visual feature vector, and converting the text information into a character feature vector; respectively calculating the similarity of the two feature vectors in each set to obtain a visual feature fusion similarity and a character feature fusion similarity; determining similarity measurement of visual feature and character feature fusion; constructing data corresponding to the original picture into an initial picture structure; allocating a weight for each edge of the initial graph structure by using the metric to obtain a simplified coarsened graph structure; performing hierarchical segmentation on the coarsened image structure to obtain a plurality of initial cut edge sets; training the automatic edge cutting set optimizer by using the training data; re-dividing the initial cut edge set by using the trained automatic cut edge set optimizer; and taking the nearest partition as a retrieval result. According to the method, the retrieval accuracy can be improved while the retrieval efficiency is ensured.

This invention belongs to the field of path planning, specifically relating to a method for obtaining the shortest path between nodes. The method first obtains the sets of cut vertices on the original graph through several traversals, and then maps the cut vertex set subgraphs corresponding to these sets of cut vertices into a high-level tree of nodes. Preprocessing and parallel shortest path queries are then performed using the high-level tree with the cut vertex set subgraphs as the smallest unit, thereby obtaining the overall shortest path. This method transforms the shortest path analysis for a large number of nodes in a topologically complex original graph into a shortest path analysis for cut vertex sets. It eliminates a large number of useless nodes when obtaining the shortest path, saving related calculations and reducing computational load, thus improving the speed of shortest path acquisition. Furthermore, preprocessing is performed on the subgraphs formed by cut vertex sets, avoiding the computationally cumbersome problem of directly obtaining the shortest path for complex topological structures of large subgraphs.