Hierarchical matching of non-rigid shapes

Dan Raviv, Anastasia Dubrovina, Ron Kimmel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - Third International Conference, SSVM 2011, Revised Selected Papers
Number of pages12
StatePublished - 2012
Event3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 - Ein-Gedi, Israel
Duration: 29 May 20112 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6667 LNCS


Conference3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011


  • Laplace-Beltrami
  • Shape correspondence
  • diffusion geometry
  • local signatures

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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