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 language | English |
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Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
Pages | 2454-2462 |
Number of pages | 9 |
ISBN (Electronic) | 9781538604571 |
DOIs | |
State | Published - Nov 6 2017 |
Event | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: Jul 21 2017 → Jul 26 2017 |
Publication series
Name | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
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Volume | 2017-January |
Conference
Conference | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
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Country/Territory | United States |
City | Honolulu |
Period | 7/21/17 → 7/26/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition