SMI 2011: Full Paper: Diffusion-geometric maximally stable component detection in deformable shapes

Authors:
Roee Litman;Alexander M. Bronstein;Michael M. Bronstein
Affiliations:
School of Electrical Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel;School of Electrical Engineering, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel;Institute of Computational Science, Faculty of Informatics, Universita della Svizzera Italiana, Lugano, Switzerland
Venue:
Computers and Graphics
Year:
2011

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Abstract

Maximally stable component detection is a very popular method for feature analysis in images, mainly due to its low computation cost and high repeatability. With the recent advance of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world. In this paper, we formulate a diffusion-geometric framework for stable component detection in non-rigid 3D shapes, which can be used for geometric feature detection and description. A quantitative evaluation of our method on the SHREC'10 feature detection benchmark shows its potential as a source of high-quality features.