185 results about "Connected component" patented technology

The invention provides a layout file geometric data processing method, a computer device and a storage medium. The method comprises the steps that a layout file of a three-dimensional chip is obtained, polygonal entities and layer information are extracted, the polygonal entities are distributed to corresponding layer containers, and an independent R tree index is created for each layer; performing connectivity analysis on the polygonal entity, and returning a connected component set and performance analysis statistical information; when an independent R-tree index is created for each layer, data of all polygonal entities of a single layer is obtained, a minimum boundary rectangle is generated for each polygonal entity, R-tree indexes are constructed for different layers in parallel by adopting a plurality of threads, whether the number of node entries of the R-tree indexes exceeds a splitting threshold value or not is judged, and if yes, splitting processing is carried out on nodes; and verifying the consistency of the created R tree index and the original data. The computer device and the storage medium can realize the method. The geometric data processing efficiency of the layout file can be improved.

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

The invention discloses an individual tree segmentation method based on machine learning, and particularly relates to the field of individual tree segmentation. The method comprises the following steps: respectively acquiring forest point cloud data through a ground scanner and an unmanned aerial vehicle, registering the forest point cloud data into a complete data set, preprocessing the point cloud data, extracting geometric, reflective and spatial distribution multi-dimensional features, and fusing the geometric, reflective and spatial distribution multi-dimensional features into a comprehensive feature vector; training a model by using a machine learning method based on the labeled data, taking the feature vector as input, and outputting whether the point belongs to a tree or not; predicting a point category by using the trained model, performing individual tree instance segmentation by using a connected component clustering and graph segmentation method through CRF post-processing optimization, constructing a 3D model and calculating key parameters; and finally, performing post-processing on a preliminary segmentation result, filtering noise by using connected region analysis, filling the deficiency by using a three-dimensional hole, optimizing a boundary based on graph segmentation, and optimizing a block seam by using an ICP (Inductively Coupled Plasma) algorithm.

Embodiments of the present disclosure relate to the field of deep learning, and provide a model evaluation method and apparatus, a device, a program, and a medium. The method comprises: acquiring a test image, the test image comprising a foreground area and a background area; inputting the test image into a target classification model, to obtain a target classification result, the target classification result at least comprising target feature information and target attribute information; inputting the test image into an attribution analysis model, to acquire an attribution graph corresponding to the test image; using the test image as a reference, analyzing a connected component in the attribution graph, and determining a type of the connected component, the type of the connected component being a foreground type or a background type; on the basis of the type of the connected component, determining whether the target classification model is a qualified target classification model, and on the basis of the type of the connected component, determining whether the target classification result is a trusted result.

The invention relates to the technical field of artificial intelligence, in particular to a question and answer method and system based on LLM fusion knowledge graph and retrieval enhancement generation. The entities which are connected with the known entities and do not appear in the local knowledge graph are marked as related entities through the graph structure connection relation, a new local knowledge graph is formed, descending sorting is carried out according to the edges and the weights in the connected components, when the weights are the same, the lexicographical order of the entity names is used as a second sorting key, and the second sorting key is used as a third sorting key; and after sorting is completed, according to the sorting sequence, until the accumulated number of the documents reaches a preset number, combining the finally obtained individual documents with the questions, and inputting the combined documents and the questions into a large language model to generate a final answer. According to the method, the accuracy and interpretability of questions and answers can be effectively improved, the illusion problem of a large language model is solved, and reliable knowledge service support is provided for a complex decision-making scene.

The invention relates to the technical field of process control, in particular to a radiation environment laboratory full-process traceable system and a processing method.According to the radiation environment laboratory full-process traceable system and processing method.By executing timestamp sequence extraction, attribute difference screening and path structure decomposition operation on detector records and shielding cover logs, causal, time sequence and role attachment relation edges between nodes are established; and classifying attribute values to construct a unified mapping set, converting the multi-source heterogeneous data into a flow chart structure with relevance, executing node embedding operation through a graph attention network algorithm, and performing adjacent node feature weighted aggregation and edge weight screening processing based on an embedding result. And calling a Tarjan strong connected component algorithm to analyze a node branch structure, identifying a heterogeneous sub-graph in a flow path by branch index difference, executing priority ranking and task insertion actions in combination with the number of available tokens and lead plate thickness grade parameters, and generating a dynamic scheduling sequence under a multi-task coexistence condition in a control path.

The invention relates to the technical field of data cleaning, in particular to an automatic heterogeneous data cleaning method and system based on a configurable rule engine. The method comprises the following steps: acquiring a heterogeneous data source, uniformly converting a data mode and an instance into a multi-dimensional heterogeneous fact graph, calculating an execution weight for each conflict rule according to the authority of the data source and the priority of the rule when a write operation conflict is detected, segmenting data partitions by utilizing the betweenness centrality of a connected component and a node, the method comprises the following steps of: generating a plurality of data partitions, forming rule subsets corresponding to the data partitions, creating isolated Drools session instances to perform parallel cleaning, generating traceability logs in the parallel cleaning process, aggregating the traceability logs after all the data partitions are cleaned, and re-evaluating and updating the authority of each data source to solve rule conflicts in the next cleaning period. According to the scheme, the writing conflict can be precisely processed, the safety of parallel cleaning is improved, the processing period is shortened, and a continuously optimized processing closed loop is formed.

The invention belongs to the technical field of robot path planning, and particularly discloses a robot path planning method based on large language model guidance, and the method comprises the steps: discretizing an environment space, and building a configuration space of a robot and an obstacle; calculating a collision area in real time and segmenting a free space into connected components; generating a prompt text; calling a large language model to analyze the text and outputting a recommended obstacle list; constructing a path tree; generating a new node; generating candidate paths based on new node backtracking, and allowing node poses to shift in a preset range through a tolerance verification mechanism; and when the target area is connected with the connected component, adding the path into the solution set library, and selecting an optimal solution according to a weighted cost function of a minimum path length and a total obstacle moving distance. The search strategy is optimized by using the semantic understanding ability of the large language model, the efficiency and adaptability of path planning in a dynamic environment are remarkably improved, and the method is suitable for scenes such as warehouse logistics and disaster rescue.

The invention discloses an Internet of Things distributed monitoring data synchronization method based on port machinery equipment state, which comprises the following steps: taking a fault sign as a node, and if the co-occurrence frequency between two nodes is greater than a preset value, constructing an undirected edge; constructing a fault group according to each connected component; acquiring image data through a plurality of information acquisition terminals, and analyzing whether a fault sign exists or not; obtaining residual fault signs in the corresponding fault group, generating a to-be-detected task, and broadcasting the to-be-detected task in the information acquisition terminal group; the information acquisition terminal calculates the spatial distance between the information acquisition terminal and each to-be-detected task, and the information acquisition terminal corresponding to the minimum value is used as a task execution terminal to confirm residual fault signs; summarizing residual fault sign confirmation conditions, generating a fault confidence coefficient, and forming a recheck message; according to the method, isolated alarm is converted into group early warning, the fault advanced sensing capability is remarkably improved, and the calculation amount of a data center is reduced through co-occurrence relation mining and group division of fault signs.

The invention discloses a CAE (Computer Aided Engineering) workflow control subsystem and method based on cloud computing, and belongs to the technical field of crossing of computer simulation and cloud computing, and the subsystem comprises a workflow configuration analysis module which is used for converting a CAE workflow configuration file possibly containing cyclic dependence into a directed acyclic graph state machine through strong connectivity component analysis; the workflow state machine operation module is used for dynamically deriving task groups which can be executed in parallel in the next stage in the state machine according to the current state; and the workflow scheduling module is used for submitting the tasks to a cloud solving platform in parallel and realizing fault recovery through state persistence. Through deep fusion of a graph theory method and a cloud computing architecture, automatic scheduling, efficient parallel execution and reliable fault tolerance of the CAE workflow are realized, and the computing resource utilization rate and the system reliability are remarkably improved.

A method for decoding a mesh includes receiving a bitstream including coded information of the mesh that includes a first group of connected components that has a first connectivity. The method includes determining at least one portion of the first connectivity. The at least one portion of the first connectivity is shared by at least one region of each connected component in the first group. The method includes for each connected component in the first group, determining attribute values of vertices in the at least one region of the respective connected component.

The invention discloses an orbit foreign matter detection method and system, and relates to the technical field of orbit detection.The method comprises the steps that an unknown foreign matter model is built through Gaussian distribution and a convex hull algorithm, and a known foreign matter model is built in combination with prior data; extracting a flat area of the ballast bed, projecting the foreign body model into a depth map and generating a shadow, and superposing to obtain a synthetic image with a mark; training a segmentation network by using the synthetic image and a real image, and obtaining a potential foreign matter mask through thermodynamic diagram binaryzation, connected domain analysis and size screening during reasoning; and inherent facility false alarms are eliminated through a secondary classification network, a real foreign matter detection result is finally output, alarm is triggered according to a detected threshold value, and efficient and accurate detection is realized. According to the method, on the premise that real foreign matter samples are scarce and ballast bed scenes are complex, efficient, accurate and real-time ballastless track foreign matter detection is achieved, meanwhile, the false alarm rate is reduced, and the generalization ability of the model to unknown foreign matter is improved.

The invention provides an online dynamic water quality fingerprint rapid acquisition and preprocessing device and method. The method comprises the steps that intelligent flow path selection is achieved through graph theory modeling, a water quality monitoring system is modeled into a directed weighted graph, bilateral connected component decomposition is carried out to recognize system vulnerabilities, and a redundant flow path is configured to improve the fault-tolerant capability; adopting an optimal transmission theory to optimize dilution control, modeling a dilution problem as an optimal transmission problem of spectral distribution, and calculating an optimal dilution ratio through a linear space super-gradient algorithm; a matrix flipping graph search algorithm is applied to accelerate spectrum calculation, a spectrum data matrix is abstracted into a flipping graph model, and an optimal matrix flipping operation sequence is found through heuristic search. According to the method, the efficiency, precision and robustness of water quality monitoring are remarkably improved, the monitoring efficiency is improved by 50%, the measurement error is reduced by 70%, and the calculation speed is improved by 5-10 times.

The invention provides an image foreign matter detection method capable of reducing false detection probability. The method comprises the following steps: searching a difference region between a to-be-detected image and a standard image; binarizing the image according to the difference region to obtain a binarized abnormal image; inputting a to-be-detected image into the false detection model, and marking false detection points and non-false detection points; binarizing the image according to the false detection points to obtain a binarized false detection image; searching a connected domain Ai and a connected domain Bj in the two binarized images respectively, wherein the connected domains are recorded as the connected domain Ai and the connected domain Bj; if the consistency of a certain connected domain Ai and any connected domain Bj meets the requirement, deleting the connected domain Ai; marking the connected domain Ai which is not deleted in the to-be-detected image as a foreign matter region; according to the method, a deep learning model classification method and an image processing method are fused, the robustness of the foreign matter detection method is enhanced, suspected areas are screened through fusion of the two methods, and the false detection rate of foreign matter detection is effectively reduced; the anti-interference capability is stronger, and the foreign matter area positioning is more accurate.

The invention discloses a fault propagation path determination method and device, a storage medium and electronic equipment, and relates to the field of Internet of Things. The method comprises the following steps: acquiring a target data set; a service calling graph is constructed according to the target data set, then strong connectivity vectors in the service calling graph are determined, and the service calling graph is used for representing a topological structure and a dependency relationship of service interaction in the distributed system; the strong connectivity vector is used for representing a connectivity component of which the bidirectional calling frequency is greater than a preset frequency and / or the data interaction strength is greater than a preset strength in the service calling graph; performing neighborhood feature aggregation on the service call graph to obtain a time sequence feature matrix; and determining a fault propagation path of the distributed system according to the strong connectivity vector and the time sequence characteristic matrix. According to the method and the device, the technical problem of poor fault propagation path prediction accuracy caused by lack of quantification of dependency relationship and topological association between services in fault propagation prediction in the prior art is solved.

The invention discloses an unmanned aerial vehicle landslide image clustering method and device for emergency monitoring, and belongs to the technical field of landslide monitoring. The method comprises the following steps: acquiring an original image shot by a camera mounted on an unmanned aerial vehicle; extracting multi-modal features of the landslide instance from the original image, wherein the multi-modal features comprise a spatial position feature, a global semantic embedding feature and a local geometric matching feature; fusing the multi-modal features by using a neural network model based on an attention mechanism to generate an instance feature vector; on the basis of the spatial position features, the local geometric matching features and the instance feature vectors, through an iteration process including a feedback mechanism, a graph structure representing the incidence relation between the landslide instances is constructed; and connected components in the graph structure are extracted, and each connected component is determined as a landslide instance cluster. According to the invention, different image instances belonging to the same landslide can be accurately associated, and the error rate of cross-frame target matching under a complex terrain is reduced.

The invention relates to the field of computer vision, in particular to a road defect self-adaptive detection method and system. The method comprises the following steps: acquiring an original road surface image, and generating a plurality of feature views by using the original road surface image; based on the feature view, performing reasoning by using a plurality of pre-constructed defect detection models to obtain an initial detection frame and a corresponding initial confidence coefficient; constructing a detection frame topological association graph by using the initial detection frame and the initial confidence coefficient, and obtaining a connected component of a potential defect target based on the detection frame topological association graph; calculating a fusion defect position and a fusion confidence coefficient based on the connected component; and carrying out adaptive updating on the defect detection model by utilizing the fusion defect position and the fusion confidence coefficient. The problems of insufficient robustness and long-term effectiveness of model identification caused by insufficient environmental adaptability, easy attenuation of model performance due to data drift and single decision-making mechanism in road defect detection in the prior art are solved.

The application relates to a key node identification method, device, equipment and medium of an electric power coupling network. A power station and a power-on relay in an electric power system are taken as an application node and a transition node of a physical layer network respectively, a logical node is constructed by the application node, a logical layer network of a structure of an undirected and weightless graph representing communication capability is built, the electric power system is modeled into a physical-logical coupling network, a logical edge centrality score in the logical layer network is calculated by using a hybrid centrality measurement, a logical edge criticality list is arranged in descending order according to the scores, a target logical edge is selected according to the sequence, corresponding transition nodes in the physical layer network are determined, a plurality of shortest paths between the two are calculated, path nodes are taken as candidate nodes to be removed, the candidate nodes and corresponding logical edges are removed, a change of a maximum connected component of the logical layer network is taken as a damage rate, and when the damage rate reaches a preset value, the removed nodes are key nodes. The method can effectively and quickly identify key nodes of an electric power network.

This invention provides a drawing processing method and system based on scene text recognition. It identifies all graphic and non-graphic elements within the global scope of a drawing. Based on the shape features of the graphic elements, it divides the drawing into several sets of non-graphic elements. Based on the connected components of all non-graphic elements within each set, it generates several text strings corresponding to those sets and performs fuzzy classification to identify the text results of the non-graphic element sets. Based on the spatial distribution information of the graphic and non-graphic elements on the drawing, it preprocesses the text results and overlays them onto the corresponding areas of the drawing, distinguishing between non-graphic and graphic elements. It also performs connectivity recognition on the non-graphic elements, initially labeling the text strings within them to obtain their corresponding text results. This achieves synchronous association recognition of graphics and text within the drawing, improving the efficiency and accuracy of drawing editing.

This invention discloses a method for generating network game optimization strategies based on a hybrid node and edge strategy. The method includes: acquiring the network topology, determining the strategy sets of the attacker and defender, and constructing a basic model of the network game; calculating the attack and defense costs of nodes and edges in the network based on the network topology and network metrics, and determining the cost constraints of the attacker and defender; considering the hybrid attack and defense strategies of nodes and edges, obtaining the strategy sets of the attacker and defender; using the maximum connected component size index to represent network performance, calculating the payoffs of the attacker and defender under each strategy profile, and obtaining the payoff matrix of the network game model; and converting the basic model of the network game into a linear programming problem for solution, obtaining the hybrid strategy Nash equilibrium solution of the attacker and defender.

An example operation may include at least one of determining, by a transformer encoder trained on annotated image-text data, first latents for a first dataset stored in a memory, and second latents for a second dataset stored in the memory, generating a similarity matrix based on comparisons between the first latents and the second latents, constructing a graph comprising nodes corresponding to the first latents and edges based on pairwise similarity exceeding a threshold, identifying connected components in the graph and selecting, from each component, at least one latent having a highest score from a classifier trained to approximate divergence between the first dataset and the second dataset, forming a reduced dataset comprising the at least one latent, providing the reduced dataset to a model training module, and training an image classifier using the reduced dataset and the second dataset.

Hardware unit (10) for determining a non-linear ground surface, the hardware unit (10) comprising: at least one data input interface (1) via which the hardware unit (10) receives measurement points (M) from a 3D point-supplying sensor (20); a computing device (2) which maps the measurement points (M) into a two-dimensional grid (G) and determines a height difference for each grid cell (C) based on the measurement points (M) assigned to that grid cell (C), and classifies one of these grid cells (C) as invalid if the respective height difference exceeds a predetermined threshold; if the respective height difference of one of these grid cells (C) does not exceed the threshold and the respective height difference of at least two grid cells (C) directly adjacent to that grid cell (C) also does not exceed the threshold, classifies that grid cell (C) as a flat surface;wherein the computing device (2) performs a connected component analysis stepwise over the grid cells (C) classified as planar surfaces, separating planar surfaces belonging to obstacles from planar surfaces belonging to a ground surface and obtaining the nonlinear ground surface; a data output interface (3) that outputs the nonlinear ground surface.

The invention discloses a water conservancy element semi-automatic labeling method based on MAFs and SAM large models, and relates to the technical field of water conservancy element semi-automatic labeling methods. The method comprises the steps of firstly performing operations such as overturning, mirroring, cutting and rotating on an unmanned aerial vehicle water conservancy element image to expand a sample data volume, then inputting water conservancy element prompt point information (pixel coordinates) by utilizing interface interaction operation, inputting an SAM Marge segmentation engine, generating an initial segmentation result, and in the face of changes of complex water conservancy elements, weeds, colors and the like, generating an initial segmentation result; the SAM initial segmentation result cannot completely distinguish the water conservancy elements from the non-water conservancy elements, in view of the result, each connected component of a binary image is calculated by using morphology, noise points of the non-water conservancy elements are filtered according to attribute information such as the water conservancy element area and the diagonal length, and finally a high-precision and high-accuracy water conservancy element sample image is obtained. The method is used for subsequent water conservancy factor semantic segmentation training.

We provide a system and method for autofocus and automated cell counting. [Solution] A system and method for autofocus using artificial intelligence includes (i) capturing multiple monochrome images over a nominal focal range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on the number of pixels associated with each connected component, (iv) generating focus quality estimates for at least some of the sorted connected components using a machine learning module, and (iv) calculating a target focal position based on the focus quality estimates of the evaluated connected components. The target focal position can be used to perform cell counting using artificial intelligence, for example, by (i) generating seed likelihood images and whole cell likelihood images based on the output of a convolutional neural network, and (ii) generating a mask indicating the quantity and / or pixel location of objects based on the seed likelihood image.

The invention relates to the technical field of complex network analysis and data mining, and provides a scientific research cooperation analysis method for edge crossing attribute network clustering, and the method comprises the steps: obtaining original data, and carrying out the preprocessing of the original data; an undirected empowerment cooperative network is constructed and processed to obtain a core sub-graph, and the maximum connected component LCC of the core sub-graph is extracted; constructing a node multi-dimensional feature vector by using a natural language processing technology; using an ECV algorithm to determine an optimal community number; constructing a topological feature matrix and an attribute similarity matrix by adopting an ANCA algorithm, and fusing the topological feature matrix and the attribute similarity matrix; hyper-parameters of the ANCA algorithm are adjusted and optimized in a grid search mode, and community division and analysis are carried out. The method has an extremely important application value and a wide social prospect in optimizing resource configuration, evaluating team influence, promoting academic communication and cooperation and guiding development of a scientific research direction.

Systems and methods for autofocus using artificial intelligence include (i) capturing a plurality of monochrome images over a nominal focus range, (ii) identifying one or more connected components within each monochrome image, (iii) sorting the identified connected components based on a number of pixels associated with each connected component, (iv) generating a focus quality estimate of at least a portion of the sorted connected components using a machine learning module, and (iv) calculating a target focus position based on the focus quality estimate of the evaluated connected components. The calculated target focus position can be used to perform cell counting using artificial intelligence, such as by (i) generating a seed likelihood image and a whole cell likelihood image based on output—a convolutional neural network and (ii) generating a mask indicative quantity and / or pixel locations of objects based on the seed likelihood image.

In various examples, perception of landmark shapes may be used for localization in autonomous systems and applications. In some embodiments, a deep neural network (DNN) is used to generate (e.g., per-point) classifications of measured 3D points (e.g., classified LiDAR points), and a representation of the shape of one or more detected landmarks is regressed from the classifications. For each of one or more classes, the classification data may be thresholded to generate a binary mask and / or dilated to generate a densified representation, and the resulting (e.g., dilated, binary) mask may be clustered into connected components that are iteratively: fitted a shape (e.g., a polynomial or Bezier spline for lane lines, a circle for top-down representations of poles or traffic lights), weighted, and merged. As such, the resulting connected components and their fitted shapes may be used to represent detected landmarks and used for localization, navigation, and / or other uses.

The application discloses a generative AI industrial design topology closed-loop optimization method and system, and relates to the technical field of computer-aided design, comprising: obtaining an initial three-dimensional geometric representation of an industrial product and design constraints, and constructing a geometric parameter offset field; in iterative deformation, calculating discrete curvature, deformation energy, time sequence mutation and topology anomaly index, triggering and positioning a topology sensitive area; in the area, performing edge collapse, edge split, local re-meshing, Loop subdivision, constraint Laplace smoothing or SDF local reconstruction, and verifying and repairing the results by Euler characteristic number, genus, boundary ring, connected component and topology anomaly; calling a physically consistent proxy model to predict performance and evaluate confidence, triggering finite element sampling when confidence or safety margin is insufficient, error risk and topology sensitive area overlap, updating the proxy model by using finite element true value increment, and finally completing layered optimization and complete finite element verification.

This invention provides a method for identifying surface defects in graphite bipolar plates, belonging to the field of defect detection technology in computer vision. First, it collects and standardizes multi-source heterogeneous image data of aligned flow channels to construct a physically consistent benchmark dataset that excludes environmental noise. Then, based on a self-supervised encoder-decoder architecture, it builds a graphite surface topology repair and reconstruction model. By using masked image modeling, the network learns prior knowledge of flow channel distribution and micro-texture, and a dynamic feature enhancement module repairs missing information in masked areas. Subsequently, it performs residual analysis on the reconstructed reference image and the actual observation image, constructing a defect discrimination feedback loop using pixel-level comparison and structural similarity measurement. This accurately identifies key defect indicators such as microcracks, edge chipping, and scratches without a standard physical template. Finally, it completes defect classification and localization through connected component quantization features, decoupling the residual mapping map into quantifiable geometric parameters, effectively reducing the false alarm rate and improving the level of detection intelligence.

The invention relates to the field of image processing, and provides a medical ultrasonic image segmentation method based on geometric guidance and concept perception fusion, which is characterized in that a geometric guidance text generation module is designed, and high-quality medical text description is automatically generated according to connected component analysis and multi-dimensional geometric analysis; according to the method, text-free segmentation is converted into text-guided segmentation, the problems that high-quality text description is limited in number, difficult to manufacture and high in cost are solved, a concept-aware cross-modal fusion module is designed, multi-scale space alignment and medical knowledge graph modeling are utilized, the problem that cross-modal fusion is insufficient is solved, and the cross-modal fusion efficiency is improved. According to explicit modeling of medical priori knowledge, professional knowledge in the medical field is fully utilized to guide a fusion process and a segmentation decision, a self-adaptive segmentation decoding module is further provided, an optimal strategy is dynamically selected according to feature complexity, the efficiency is improved while the precision is ensured, and the method is suitable for large-scale popularization and application. According to the invention, the precision and efficiency of medical ultrasonic image segmentation are improved.