The invention discloses a new method for extracting and classifying an expandable grid curved surface, and the method comprises the steps: extracting an expandable region after a triangular grid curved surface model is built; searching a boundary top point position for building a strip region from the expandable region; carrying out the building of the strip region through the boundary top point position; carrying out the ruled surface equation fitting of the strip region, and judging whether a ruled surface equation meets an expandable condition or not: determining the expandable type if the ruled surface equation meets the expandable condition, or else, dividing the strip region into two parts; and carrying out the ruled surface equation fitting of the strip region again till the expandable condition is met. The method has the capability of recognizing a tangent curved surface, and can extract a continuous cylindrical surface and a continuous conical surface. Various types of recognized expandable curved surfaces are very close to the real expandable region of the model. The result of the method is more beneficial for the precise segmenting and classification of the extracted expandable curved surface grids according to the expandable type and the processing of grids later.

New method for extracting and classifying expandable grid curved surface

The invention discloses a grid-based spatial multi-scale fast clustering method. The method includes the following steps that: S1, a data scale is selected, the size of grids is determined, gridding is performed on sample data, and the density values of the grids is put into statistics; S2, an initial density threshold is specified, all grids satisfying the threshold condition are reserved, and apreliminary density matrix is obtained; S3, a filter template is specified according to an observation scale, and convolution operation is performed on a global grid space; S4, a connected region is generated through neighborhood search so as to be adopted as a preliminary clustering result, integration operation is performed on the grids, the grid space is mapped onto an original point set, and an original point set clustering result is obtained; S5, the observation scale is adjusted, a transformed new filter is adopted to perform operation in the S3 and S4 on a result matrix again, a clustering result of the next observation scale is obtained; and S6, the data scale is changed, the S1 to S5 are repeated, clustering results under different data scales are obtained. The algorithm of the invention has the advantages of low complexity, high clustering efficiency and high precision, and can meet the requirements of real-time multi-scale clustering and visual analysis of massive point sets.

Grid-based spatial multi-scale fast clustering method

The invention relates to the technical field of three-dimensional image processing, and especially relates to a method for the interactive segmentation of a full-jaw tooth triangular network model. According to the invention, a segmentation line of tooth gingiva is selected through combining various types of model data. Moreover, the method accords with a modular idea, does not carry out any smoothening or subdividing of a decomposition result, can guarantee that a segmented model accords with actual teeth to maximum degree, is assisted manually, and can improves the accuracy and efficiency.

Method for interactive segmentation of full-jaw tooth triangular network model

The invention discloses a large-scale dynamic graph division method based on a sliding window, and belongs to the technical field of computers. According to the invention, when the vertexes are increased, by preferentially selecting the vertexes with the relatively higher degrees in the sliding window to divide the vertexes, the vertexes with the small degrees can be gathered to the vertexes withthe large degrees, the vertexes as many as possible can be divided into the appropriate partitions at each time of division, the load balancing is achieved, at the same time the number of the cut edges is reduced, so that the communication cost during the graph calculation process is greatly reduced; when the edges are increased, by preferentially selecting the vertex with the most adjacent edgesin the sliding window for division, the frequent vertex migration can be effectively avoided, and the adjacent vertexes can be divided into the appropriate partitions as many as possible at each timeof division, so that the vertex migration frequency is greatly reduced, the division efficiency is improved, and the load balancing and the minimization of the edge cutting number are realized.

Large-scale dynamic graph division method based on sliding window

The invention belongs to the field of computer graphic processing, particularly relates to a quadratic surface element extraction method, system and device based on hierarchical clustering, and aims to solve the problem that simple quadratic surface element detection is uncertain. The method comprises the steps that triangular grids of a three-dimensional model are acquired, each triangle serves as a clustering area, and a clustering area set is obtained; respectively taking the clustering areas adjacent to two sides as an ordered pair, and respectively calculating the fitting cost of each ordered pair; selecting a group of ordered pairs with the minimum fitting cost, and fitting the corresponding clustering areas to serve as new clustering areas to update a clustering area set; based on the new cluster region set, iteratively calculating the fitting cost of each cluster region according to preset iteration times, fitting the fitting cost, and obtaining the fitting result of each typeof cluster region; and extracting a quadric surface element based on the obtained fitting result. The invention provides an efficient quadratic surface element detection method, and high-quality simple quadratic surface elements are extracted.

Quadratic surface element extraction method, system and device based on hierarchical clustering

The invention discloses a hierarchical grid partition method under multi-scale precision control The method includes: calculating shape information corresponding to each partition result; using the shape information calculated in the last time for guidance during iteration partition in the next time; taking local geometrical information of a model into consideration during partition to complementally deal with a condition that a partitioned area is too complex to describe with a simple shape; when partition of each hierarchy is finished, judging according to a precision control strategy, if the partitioned area meets a precision standard in the hierarchy, determining that the partition result is acceptable, and dropping out of subsequent partition, wherein the precision control strategy adopts multi-scale control to guarantee hierarchical partition effects during iteration partition The hierarchical grid partition method under multi-scale precision control has the advantages of improvement of partition precision, realization of respective partition of different scales of characteristics in the model and adaptability to different demands of various fields

Hierarchical grid partition method under multi-scale precision control

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Hierarchical grid partition method under multi-scale precision control