@inproceedings{999a13087b704b4290599f9c9afe5d1b,
title = "Hierarchical matching of non-rigid shapes",
abstract = "Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision. While rigid alignment can be parameterized using a small number of unknowns representing rotations, reflections and translations, non-rigid alignment does not have this advantage. The majority of the methods addressing this problem boil down to a minimization of a distortion measure. The complexity of a matching process is exponential by nature, but it can be heuristically reduced to a quadratic or even linear for shapes which are smooth two-manifolds. Here we model shapes using both local and global structures, and provide a hierarchical framework for the quadratic matching problem.",
keywords = "Laplace-Beltrami, Shape correspondence, diffusion geometry, local signatures",
author = "Dan Raviv and Anastasia Dubrovina and Ron Kimmel",
year = "2012",
doi = "10.1007/978-3-642-24785-9_51",
language = "الإنجليزيّة",
isbn = "9783642247842",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "604--615",
booktitle = "Scale Space and Variational Methods in Computer Vision - Third International Conference, SSVM 2011, Revised Selected Papers",
note = "3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 ; Conference date: 29-05-2011 Through 02-06-2011",
}