10 results about "Disjoint-set" patented technology

This invention relates to the field of computer vision technology, specifically disclosing a disparity detection method based on synchronous filtering. The method comprises the following steps: S1: feature extraction and cross-view point pair construction; S2: constructing a disjoint-set data structure with dynamic member tracking capabilities; S3: constructing a synchronous filtering sequence based on global ranking; S4: dynamic topological consistency analysis and disparity suspect point marking; S5: filtering to obtain highly robust final disparity feature points and outputting them. The technical solution of this invention can accurately identify and retain disparity feature points that have physical consistency but violate the global geometric model in scenarios with large disparity and complex depth, without pre-setting a global geometric model or relying on fixed parameters.

Systems and methods for detecting a presentation attack. Datasets of original images and attack samples are used to train a model that creates in a feature space of samples of original images a set of elements corresponding to each sample. Elements form a limited set normalized to a disjoint set so that the boundary of the disjoint set divides the space of all possible collections of features into “legitimate” and “all others.”“All others” include signs of known attacks, anomalies and signs of unknown attacks.

The application discloses a kind of based on adaptive boundary perception's spare part point cloud hierarchical super voxel segmentation method, it is related to three-dimensional point cloud super voxel segmentation technical field, this method calculates the multidimensional geometry feature of each point of point cloud first, constructs point cloud boundary weight model by weighted fusion and nonlinear transformation;Again, complete point cloud voxelization based on octree space division mechanism, according to boundary weight value, voxel is divided into boundary voxel and seed voxel, and the initial super voxel is generated by merging the point set in voxel through union-find set data structure;Then, relying on global energy optimization framework, combined with distance measurement function containing boundary weight difference item, hierarchical iterative fusion is carried out on the representative point of initial super voxel;Finally, the super voxel after fusion is corrected and the spatial connectivity is corrected, and the multi-resolution super voxel segmentation result is output.The application can solve the problem that traditional method is easy to over-segmentation, under-segmentation, and enhance the adaptive level of complex equipment spare part structure.

This invention discloses an intelligent detection method and system for bid format compliance of sealed bid documents, relating to the field of electronic bidding. The method includes: performing format detection based on the format rules of the bidding documents to obtain a set of violation records and a single format detection report; constructing a format error fingerprint vector, calculating cross-file error similarity, and generating a cross-file error similarity matrix; constructing an initial suspected association graph using each sealed bid document as a node and the similarity values ​​in the cross-file error similarity matrix as edge weights between nodes; dynamically adjusting the connectivity threshold, updating the node connection relationships in the weighted disjoint-set data structure, and defining the groups divided under the optimal connectivity threshold as abnormal similar file groups; calculating a bid-rigging suspicion score, generating a bid-rigging detection report, and outputting the bid compliance detection results. This solves the technical problem in existing technologies where isolated compliance judgments on individual bid documents fail to identify potential bid-rigging behavior.

The invention discloses a class increment classification method based on union search set graph modeling and fusion space, which comprises the following steps: configuring an independent adaptation module for each increment stage, extracting class characteristics of each stage by freezing a pre-training visual backbone network, and constructing a stage class prototype set. And calculating cross-stage similarity among global category prototypes, establishing a category confusion relationship, and identifying confusion connected branches by using a union-check set structure. And for each connected branch, fusing the features of the adaptive module in the corresponding increment stage and optimizing the fusion weight to form a fusion feature space. In the inference stage, the preliminary classification result is used for judging whether the candidate category belongs to a confusion connected branch or not, if yes, fine judgment is carried out in the fusion feature space to output a final result, and if not, the preliminary classification result is directly output. The method effectively solves the identification problem of cross-stage easily-confused categories, improves the classification precision, and maintains the low incremental training cost and the anti-forgetting capability of the model at the same time.

The invention discloses a global track identity management method and device for roadside multi-sensor collaborative awareness, and relates to the technical field of target tracking, and the method comprises the steps: monitoring a target through a thunder-vision all-in-one machine, and generating a track corresponding to the target; when it is detected that two tracks monitored by different Leiyu all-in-one machines belong to the same target and need to be associated, a root node query function is adopted to check whether the two tracks have association conflicts, if the association conflicts exist, the association conflicts are dynamically adjusted, looked up and eliminated, and if the association conflicts do not exist, the association conflicts do not exist; if yes, associating the two tracks belonging to the same target; and for the two tracks of which the association conflict is eliminated, searching and searching sets, determining timestamps of root identities corresponding to the two tracks, taking the root identity corresponding to the timestamp with a small numerical value as the identity of the associated track, merging the tracks corresponding to the timestamp with a large numerical value into the track corresponding to the timestamp with a small numerical value, and carrying out association. The operation complexity can be remarkably reduced, and the system operation efficiency can be improved.

The application provides an interval event matching method and system of a content-based publish / subscribe system, through constructing a predicate disjoint set and an optimized partition model thereof, and integrating a learning index of a range query recursive model index in each partition, high-speed, stable and verifiable matching of events to a subscription set under multi-attribute interval predicates is realized; in particular, the traditional memory-intensive matching is reconstructed into a calculation-intensive process mainly based on model reasoning, while correctness and completeness are ensured, average latency is reduced and long tail delay is inhibited, and key modules such as partition and coverage optimization, model training and online reasoning, error boundary control and limited correction, and accurate back matching of residual set subscription are included.

The invention relates to a method for detecting linear objects in a railway network, comprising: for each point in a point cloud, calculation (22) of at least one corresponding geometric descriptor; assignment (24) of each point to a class, based on the corresponding geometric descriptors; detection (28) of planes defined by all or part of the points of a class of interest; consolidation (30) of the class of interest, including the exclusion of each point not belonging to a detected plane; agglomeration (32) of the points of the consolidated class of interest into disjoint sets of points, each representative of a respective linear object; determination (34) of the height of each linear object from the coordinate sets of the respective points; and association (38), to a predetermined target category, of each linear object whose height belongs to a corresponding predetermined height interval. Figure 1

The invention discloses a power distribution network joint layout optimization method based on reinforcement learning, and the method comprises the following steps: S1, building a power distribution network initial graph model containing source-load distribution, and defining a joint planning Markov decision process of a line and a switch; s2, constructing a decision-making agent based on a graph convolutional network and near-end strategy optimization; and S3, carrying out topological constraint processing and model training based on a union-check set, maintaining connected components of nodes of the power distribution network through the union-check set, generating a dynamic topological constraint mask, and completing optimization training and scheme output of the joint layout of the power distribution network in combination with the decision-making agent. According to the reinforcement learning-based power distribution network joint layout optimization method provided by the invention, collaborative automatic planning of lines and switches can be realized, and the economical efficiency and the reliability of the power distribution network are remarkably improved on the premise of meeting various technical constraints.