Group-wise point-set registration based on Rényi's Second Order Entropy

Luis G.Sanchez Giraldo, Erion Hasanbelliu, Murali Rao, Jose C. Principe

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

10 Scopus citations

Abstract

In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set. These methods mitigate the need to establish a correspondence among the point-sets by representing them as probability density functions where the registration is treated as a multiple distribution alignment. Holder's and Jensen's inequalities provide a notion of similarity/distance among point-sets and Rényi's second order entropy yields a closed-form solution to the cost function and update equations. We also show that the methods can be improved by normalizing the entropy with a scale factor. These provide simple, fast and accurate algorithms to compute the spatial transformation function needed to register multiple point-sets. The algorithms are compared against two well-known methods for group-wise point-set registration. The results show an improvement in both accuracy and computational complexity.

Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Pages2454-2462
Number of pages9
ISBN (Electronic)9781538604571
DOIs
StatePublished - Nov 6 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States
Duration: Jul 21 2017Jul 26 2017

Publication series

NameProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Volume2017-January

Conference

Conference30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Country/TerritoryUnited States
CityHonolulu
Period7/21/177/26/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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