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Technical Program
Paper Detail
| Paper: | TP-P7.11 |
| Session: | Image and Video Modeling |
| Time: | Tuesday, October 10, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
A PROFILE HIDDEN MARKOV MODEL FRAMEWORK FOR MODELING AND ANALYSIS OF SHAPE |
| Authors: |
Rui Huang; Rutgers University | | | | | Vladimir Pavlovic; Rutgers University | | | | | Dimitris Metaxas; Rutgers University | | |
| Abstract: |
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. |
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