TY - GEN
T1 - Group-wise point-set registration based on Rényi's Second Order Entropy
AU - Giraldo, Luis G.Sanchez
AU - Hasanbelliu, Erion
AU - Rao, Murali
AU - Principe, Jose C.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85044265606&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044265606&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2017.263
DO - 10.1109/CVPR.2017.263
M3 - Conference contribution
AN - SCOPUS:85044265606
T3 - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
SP - 2454
EP - 2462
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Y2 - 21 July 2017 through 26 July 2017
ER -