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CLC number: TP391.4

On-line Access: 2012-06-05

Received: 2011-11-01

Revision Accepted: 2011-12-14

Crosschecked: 2012-05-04

Cited: 7

Clicked: 7667

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.6 P.440-451


Feature detection of triangular meshes via neighbor supporting

Author(s):  Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun

Affiliation(s):  School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China; more

Corresponding email(s):   wangxiaochao18@gmail.com, xpliu@dlut.edu.cn

Key Words:  Feature detection, Neighbor supporting, Normal tensor voting, Salient measure

Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun. Feature detection of triangular meshes via neighbor supporting[J]. Journal of Zhejiang University Science C, 2012, 13(6): 440-451.

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author="Xiao-chao Wang, Jun-jie Cao, Xiu-ping Liu, Bao-jun Li, Xi-quan Shi, Yi-zhen Sun",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%T Feature detection of triangular meshes via neighbor supporting
%A Xiao-chao Wang
%A Jun-jie Cao
%A Xiu-ping Liu
%A Bao-jun Li
%A Xi-quan Shi
%A Yi-zhen Sun
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 6
%P 440-451
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100324

T1 - Feature detection of triangular meshes via neighbor supporting
A1 - Xiao-chao Wang
A1 - Jun-jie Cao
A1 - Xiu-ping Liu
A1 - Bao-jun Li
A1 - Xi-quan Shi
A1 - Yi-zhen Sun
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 6
SP - 440
EP - 451
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100324

We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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