2010/12/17 by Roee Litman, Alex M. Bronstein, Alexander M. Bronstein +1 · 55 citations
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Algorithm #Artificial intelligence #Benchmark (surveying) #Component (thermodynamics) #Computation #Computer science #Computer vision #Diffusion #Feature (linguistics) #Feature detection (computer vision) #Geometric shape #Geometry #Image (mathematics) #Image Processing and 3D Reconstruction #Image Retrieval and Classification Techniques #Image processing #Mathematics #Pattern recognition (psychology) #Physics #Repeatability #Statistics #cs.CV
paper · pdf · doi:10.1016/j.cag.2011.03.011
published in Computers & Graphics 35(3), 549-560 (Elsevier BV)
arxiv created 2010/12/17 · openalex publication_date 2011/04/14 · arxiv updated 2014/06/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.