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Manifold Intrinsic Similarity

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Handbook of Mathematical Methods in Imaging

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Abstract

Nonrigid shapes are ubiquitous in nature and are encountered at all levels of life, from macro to nano. The need to model such shapes and understand their behavior arises in many applications in imaging sciences, pattern recognition, computer vision, and computer graphics. Of particular importance is understanding which properties of the shape are attributed to deformations and which are invariant, i.e., remain unchanged. This chapter presents an approach to nonrigid shapes from the point of view of metric geometry. Modeling shapes as metric spaces, one can pose the problem of shape similarity as the similarity of metric spaces and harness tools from theoretical metric geometry for the computation of such a similarity.

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  1. Computer Science Department, Technion-Israel Institute of Technology, 110 8th Street, 32000, Haifa, Israel

    Alexander M. Bronstein & Michael M. Bronstein

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Bronstein, A.M., Bronstein, M.M. (2015). Manifold Intrinsic Similarity. In: Scherzer, O. (eds) Handbook of Mathematical Methods in Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0790-8_57

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