Literature DB >> 19110497

Information geometry for landmark shape analysis: unifying shape representation and deformation.

Adrian M Peter1, Anand Rangarajan.   

Abstract

Shape matching plays a prominent role in the comparison of similar structures. We present a unifying framework for shape matching that uses mixture models to couple both the shape representation and deformation. The theoretical foundation is drawn from information geometry wherein information matrices are used to establish intrinsic distances between parametric densities. When a parameterized probability density function is used to represent a landmark-based shape, the modes of deformation are automatically established through the information matrix of the density. We first show that given two shapes parameterized by Gaussian mixture models (GMMs), the well-known Fisher information matrix of the mixture model is also a Riemannian metric (actually, the Fisher-Rao Riemannian metric) and can therefore be used for computing shape geodesics. The Fisher-Rao metric has the advantage of being an intrinsic metric and invariant to reparameterization. The geodesicâcomputed using this metricâestablishes an intrinsic deformation between the shapes, thus unifying both shape representation and deformation. A fundamental drawback of the Fisher-Rao metric is that it is not available in closed form for the GMM. Consequently, shape comparisons are computationally very expensive. To address this, we develop a new Riemannian metric based on generalized \phi-entropy measures. In sharp contrast to the Fisher-Rao metric, the new metric is available in closed form. Geodesic computations using the new metric are considerably more efficient. We validate the performance and discriminative capabilities of these new information geometry-based metrics by pairwise matching of corpus callosum shapes. We also study the deformations of fish shapes that have various topological properties. A comprehensive comparative analysis is also provided using other landmark-based distances, including the Hausdorff distance, the Procrustes metric, landmark-based diffeomorphisms, and the bending energies of the thin-plate (TPS) and Wendland splines.

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Year:  2009        PMID: 19110497      PMCID: PMC2921979          DOI: 10.1109/TPAMI.2008.69

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  11 in total

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2.  Counting probability distributions: differential geometry and model selection.

Authors:  I J Myung; V Balasubramanian; M A Pitt
Journal:  Proc Natl Acad Sci U S A       Date:  2000-10-10       Impact factor: 11.205

3.  Shape L'Âne Rouge: Sliding Wavelets for Indexing and Retrieval.

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Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2008

4.  3-D diffeomorphic shape registration on hippocampal data sets.

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Journal:  Med Image Comput Comput Assist Interv       Date:  2005

5.  Statistical shape analysis: clustering, learning, and testing.

Authors:  A Srivastava; S H Joshi; W Mio
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2005-04       Impact factor: 6.226

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Authors:  Yue Wang; Kelvin Woods; Maxine McClain
Journal:  IEEE Trans Image Process       Date:  2002       Impact factor: 10.856

7.  Landmark matching via large deformation diffeomorphisms.

Authors:  S C Joshi; M I Miller
Journal:  IEEE Trans Image Process       Date:  2000       Impact factor: 10.856

8.  A Robust Algorithm for Point Set Registration Using Mixture of Gaussians.

Authors:  Bing Jian; Baba C Vemuri
Journal:  Proc IEEE Int Conf Comput Vis       Date:  2005-10

9.  A Novel Representation for Riemannian Analysis of Elastic Curves in ℝ

Authors:  Shantanu H Joshi; Eric Klassen; Anuj Srivastava; Ian Jermyn
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2007-07-16

10.  A surface-based technique for warping three-dimensional images of the brain.

Authors:  P Thompson; A W Toga
Journal:  IEEE Trans Med Imaging       Date:  1996       Impact factor: 10.048

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  3 in total

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Authors:  Frank Nielsen
Journal:  Entropy (Basel)       Date:  2019-05-11       Impact factor: 2.524

3.  Spherical Minimum Description Length.

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Journal:  Entropy (Basel)       Date:  2018-08-03       Impact factor: 2.524

  3 in total

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