Method for abstracting grade framework and stereo decomposing of arborescence figure

Abstract

The invention discloses a dendriform solid decomposition and graded framework extraction method, which comprises the following steps: a maximum central point diagram is constructed through integral-type boundary distance conversion and integral-type single seed point distance conversion of a solid; a dendriform clustering scheme is generated, the clustering scheme is combined with the maximum central point diagram to extract the central representative point of each clustering, and a classification framework is constructed for classification; branch points are taken as seed points for integral type distance conversion, thus constructing the section of each branch; equidistant faces of the sections are constructed through curved surface unidirectional distance conversion, thus acquiring divisional planes and completing decomposition of the solid, namely, the solid is decomposed into a plurality of blocks; finally, a three-dimensional weighted directed graph is constructed through precise Euclidean distance conversion, the cost value of a compact center is calculated, and the minimal spanning tree is extracted; the central frameworks of the blocks and a connecting framework reaching the centers of the divisional planes are acquired, thus finally getting the graded framework. The invention can measure form and structure, and represent shapes accurately according to test specifications of a plurality of data sets.

Description

technical field [0001] The invention belongs to the interdisciplinary technical field of the combination of pattern recognition and scientific calculation visualization, and relates to three-dimensional decomposition and hierarchical skeleton extraction technology of a tree-like shape (abbreviated as shape). Background technique [0002] With the rapid development of data acquisition technology, the resolution of image data used in computer graphics, computer-aided design technology and medical image system is getting higher and higher, and the amount of data is also increasing. As a feature extraction and feature representation method, skeletonization can effectively remove redundant information and retain feature information. In virtual reality, skeletalization can extract paths from data to guide walkthroughs and prevent getting lost during walkthroughs. For forestry visualization and measurement, the skeleton can accurately represent the topology of the shape for tree i...

AI Technical Summary

Problems solved by technology

However, these three methods cannot extract the hierarchical skeleton of a three-dimensional solid body with good centrality, so they cannot solve the problems of representation, measurement and recognition of three-dimensional solid bodies.

Katz et al. decompose the mesh surface into simple patches according to the concave-convex and non-convex characteristics of the surface, but it cannot be used to find the skeleton.

Images