SHAPE ANALYSIS USING CURVATURE-BASED DESCRIPTORS AND PROFILE HIDDEN MARKOV MODELS

This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A profile hidden Markov model (PHMM) is then built on such descriptors to represent a class of similar shapes. 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 parts. The sparseness of the PHMM structure also provides efficient inference and learning algorithms for shape modeling and analysis. Our experimental results on corpus callosum images show the effectiveness and robustness of this new framework.