Feature detection of triangular meshes based on tensor voting theory

被引：108

作者：

Kim, Hyun Soo ^{[1
]}

Choi, Han Kyun ^{[1
]}

Lee, Kwan H. ^{[1
]}

机构：

[1] Gwangju Inst Sci & Technol, Kwangju 500712, South Korea

来源：

COMPUTER-AIDED DESIGN | 2009年 / 41卷 / 01期

关键词：

Triangular mesh; Feature detection; Segmentation; Tensor voting; Clustering; LINES; CLASSIFICATION;

D O I：

10.1016/j.cad.2008.12.003

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper presents n-dimensional feature recognition of triangular meshes that can handle both geometric properties and additional attributes such as color information of a physical object. Our method is based on a tensor voting technique for classifying features and integrates a clustering and region growing methodology for segmenting a mesh into sub-patches. We classify a feature into a corner, a sharp edge and a face. Then, finally we detect features via region merging and cleaning processes. Our feature detection shows good performance with efficiency for various dimensional models. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.

引用

页码：47 / 58

页数：12