TY - GEN
T1 - Groupwise shape registration based on entropy minimization
AU - Kee, Youngwook
AU - Cremers, Daniel
AU - Kim, Junmo
PY - 2012
Y1 - 2012
N2 - In this paper, we propose a unified framework for global-to-local groupwise shape registration based on an unbiased diffeomorphic shape atlas. We introduce the information-theoretic concept of entropy as the energy functional for shape registration. To this end, for given example shapes, we estimate the underlying shape distribution on the space of signed distance functions in a nonparametric way. We then perform global-to-local shape registration by minimizing the shape entropy estimate and entropy of displacement vector field. In addition, the gradient flow for the shape entropy is derived explicitly using the L 2-distance in Hilbert space for a template shape estimation. Diffeomorphisms which are estimated by rigid/nonrigid registrations obviously establish dense correspondences between an example shape and the template shape. In addition, the composition rule gives a way to establish consistent correspondences by guaranteeing another diffeomorphism.
AB - In this paper, we propose a unified framework for global-to-local groupwise shape registration based on an unbiased diffeomorphic shape atlas. We introduce the information-theoretic concept of entropy as the energy functional for shape registration. To this end, for given example shapes, we estimate the underlying shape distribution on the space of signed distance functions in a nonparametric way. We then perform global-to-local shape registration by minimizing the shape entropy estimate and entropy of displacement vector field. In addition, the gradient flow for the shape entropy is derived explicitly using the L 2-distance in Hilbert space for a template shape estimation. Diffeomorphisms which are estimated by rigid/nonrigid registrations obviously establish dense correspondences between an example shape and the template shape. In addition, the composition rule gives a way to establish consistent correspondences by guaranteeing another diffeomorphism.
UR - http://www.scopus.com/inward/record.url?scp=84865830864&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32717-9_35
DO - 10.1007/978-3-642-32717-9_35
M3 - Conference contribution
AN - SCOPUS:84865830864
SN - 9783642327162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 347
EP - 356
BT - Pattern Recognition - Joint 34th DAGM and 36th OAGM Symposium, Proceedings
T2 - Joint 34th Symposium of the German Association for Pattern Recognition, DAGM 2012 and 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012
Y2 - 28 August 2012 through 31 August 2012
ER -