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Proceedings Paper

Using shape distributions as priors in a curve evolution framework
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Paper Abstract

In this paper we propose a framework of constructing and using a shape prior in estimation problems. The key novelty of our technique is a new way to use high level, global shape knowledge to derive a local driving force in a curve evolution context. We capture information about shape in the form of a family of shape distributions (cumulative distribution functions) of features related to the shape. We design a prior objective function that penalizes the differences between model shape distributions and those of an estimate. We incorporate this prior in a curve evolution formulation for function minimization. Shape distribution-based representations are shown to satisfy several desired properties, such as robustness and invariance. They also have good discriminative and generalizing properties. To our knowledge, shape distribution-based representations have only been used for shape classification. Our work represents the development of a tractable framework for their incorporation in estimation problems. We apply our framework to three applications: shape morphing, average shape calculation, and image segmentation.

Paper Details

Date Published: 21 May 2004
PDF: 12 pages
Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.525410
Show Author Affiliations
Andrew V. Litvin, Boston Univ. (United States)
William Clement Karl, Boston Univ. (United States)

Published in SPIE Proceedings Vol. 5299:
Computational Imaging II
Charles A. Bouman; Eric L. Miller, Editor(s)

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