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Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis
September/October 2004 (vol. 10 no. 5)
pp. 516-524
ASCII Text
x 
 
Ulrich Clarenz, Martin Rumpf, Alexandru Telea, "Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis," IEEE Transactions on Visualization and Computer Graphics, vol. 10, no. 5, pp. 516-524, September/October, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2004.34,
author = {Ulrich Clarenz and Martin Rumpf and Alexandru Telea},
title = {Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {10},
number = {5},
issn = {1077-2626},
year = {2004},
pages = {516-524},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2004.34},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis
IS - 5
SN - 1077-2626
SP516
EP524
EPD - 516-524
A1 - Ulrich Clarenz,
A1 - Martin Rumpf,
A1 - Alexandru Telea,
PY - 2004
KW - Surface classification
KW - surface processing
KW - edge detection
KW - nonsmooth geometry.
VL - 10
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.

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Index Terms:
Surface classification, surface processing, edge detection, nonsmooth geometry.
Citation:
Ulrich Clarenz, Martin Rumpf, Alexandru Telea, "Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis," IEEE Transactions on Visualization and Computer Graphics, vol. 10, no. 5, pp. 516-524, Sept.-Oct. 2004, doi:10.1109/TVCG.2004.34
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