Embedded Profile Hidden Markov Models for Shape Analysis

An ideal shape model should be both invariant to global transformations and robust to local distortions. In this paper we present a new shape modeling framework that achieves both efficiently. 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 provides efficient inference and learning algorithms for shape modeling and analysis. To capture the global characteristics of a class of shapes, the PHMM parameters are further embedded into a subspace that models long term spatial dependencies. The new framework can be applied to a wide range of problems, such as shape matching/registration, classificati...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
• Keywords: Computer Vision, Curvature-based Shape Descriptor, ICCV 2007, Ideal Shape Model, Shape Contour Perturbations, Shape Instance, Shape Modeling Framework