A profile hidden Markov model framework for modeling and analysis of shape

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2121-2124
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Hidden Markov models
  • Image shape analysis

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