Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications

Kee Y, Lee HS, Yim J, Cremers D, Kim J (2015)

Publication Type: Journal article

Publication year: 2015

Journal

IEEE Signal Processing Letters Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 22

Pages Range: 1922-1926

Article Number: 7118138

Journal Issue: 11

DOI: 10.1109/LSP.2015.2441745

Abstract

We propose an information-theoretic criterion, entropy estimate, for the joint alignment of a group of shape observations drawn from an unknown shape distribution. Employing a nonparametric density estimation technique with implicit shape representation, we minimize the entropy estimate with respect to the pose parameters of similarity transformations based on gradient descent optimization for which we provide implementation details. We demonstrate the capacity of our approach in numerous experiments with an application of building a shape prior to prostate MR image segmentation.

Involved external institutions

Korea Advanced Institute of Science and Technology (KAIST)

Korea, Republic of (KR) Technische Universität München (TUM)

Germany (DE)

How to cite

APA:

Kee, Y., Lee, H.S., Yim, J., Cremers, D., & Kim, J. (2015). Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications. IEEE Signal Processing Letters, 22(11), 1922-1926. https://dx.doi.org/10.1109/LSP.2015.2441745

MLA:

Kee, Youngwook, et al. "Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications." IEEE Signal Processing Letters 22.11 (2015): 1922-1926.

BibTeX: Download