A correspondence-less approach to matching of deformable shapes

Jonathan Pokrass, Alexander M. Bronstein, Michael M. Bronstein

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

Abstract

Finding a match between partially available deformable shapes is a challenging problem with numerous applications. The problem is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise correspondence between the two. In this paper, we introduce an alternative correspondence-less approach to matching fragments to an entire shape undergoing a non-rigid deformation. We use diffusion geometric descriptors and optimize over the integration domains on which the integral descriptors of the two parts match. The problem is regularized using the Mumford-Shah functional. We show an efficient discretization based on the Ambrosio-Tortorelli approximation generalized to triangular meshes. Experiments demonstrating the success of the proposed method are presented.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - Third International Conference, SSVM 2011, Revised Selected Papers
Pages592-603
Number of pages12
DOIs
StatePublished - 2012
Externally publishedYes
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

Conference

Conference3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011
Country/TerritoryIsrael
CityEin-Gedi
Period29/05/112/06/11

Keywords

  • Laplace-Beltrami operator
  • Mumford-Shah regularization
  • deformable shapes
  • diffusion geometry
  • heat kernel signature
  • partial correspondence
  • partial matching
  • partial similarity
  • shape descriptors

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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