1. Diffusion-geometric maximally stable component detection in deformable shapes
- Author
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Roee Litman, Michael M. Bronstein, and Alexander M. Bronstein
- Subjects
FOS: Computer and information sciences ,business.industry ,Computer Vision and Pattern Recognition (cs.CV) ,Computation ,Computer Science - Computer Vision and Pattern Recognition ,I.4.7 ,General Engineering ,Pattern recognition ,I.4.8 ,Geometric shape ,3d shapes ,Computer Graphics and Computer-Aided Design ,Full paper ,Human-Computer Interaction ,Diffusion geometry ,Artificial intelligence ,business ,Mathematics ,Shape analysis (digital geometry) - 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.
- Published
- 2011