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A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape

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A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape
In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a Profile Hidden Markov Model (PHMM) representation of the shape. PHMMs are a particular type of Hidden Markov Models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
Added 22 Oct 2009
Updated 22 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
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