<P>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 rep...
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https://www.riss.kr/link?id=A107736867
2015
-
SCOPUS,SCIE
학술저널
1922-1926(5쪽)
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
<P>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 rep...
<P>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.</P>