DocumentCode
3328091
Title
Multi-scale Curve Detection on Surfaces
Author
Kolomenkin, Michael ; Shimshoni, Ilan ; Tal, Avishay
fYear
2013
fDate
23-28 June 2013
Firstpage
225
Lastpage
232
Abstract
This paper extends to surfaces the multi-scale approach of edge detection on images. The common practice for detecting curves on surfaces requires the user to first select the scale of the features, apply an appropriate smoothing, and detect the edges on the smoothed surface. This approach suffers from two drawbacks. First, it relies on a hidden assumption that all the features on the surface are of the same scale. Second, manual user intervention is required. In this paper, we propose a general framework for automatically detecting the optimal scale for each point on the surface. We smooth the surface at each point according to this optimal scale and run the curve detection algorithm on the resulting surface. Our multi-scale algorithm solves the two disadvantages of the single-scale approach mentioned above. We demonstrate how to realize our approach on two commonly-used special cases: ridges & valleys and relief edges. In each case, the optimal scale is found in accordance with the mathematical definition of the curve.
Keywords
edge detection; feature extraction; appropriate smoothing; curve detection algorithm; edge detection; general framework; multiscale algorithm; multiscale curve detection; optimal scale detection; smoothed surface; Equations; Feature extraction; Image edge detection; Mathematical model; Smoothing methods; Surface treatment; Three-dimensional displays; 3D; Curves; Multiscale;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location
Portland, OR
ISSN
1063-6919
Type
conf
DOI
10.1109/CVPR.2013.36
Filename
6618880
Link To Document