Abstract

this paper, we develop a hierarchical statistical modeling framework for the representation, segmentation, and tracking of 2D deformable structures in image sequences. The model relies on the specification of a template, on which global as well as local deformations are defined. Global deformations are modeled using a statistical modal analysis of the deformations observed on a representative population. Local deformations are represented by a (first-order) Markov random process. A model-based segmentation of the scene is obtained by a joint bayesian estimation of global deformation parameters and local deformation variables. Spatial or spatio-temporal observations are considered in this estimation procedure, yielding an edge-based or a motion-based segmentation of the scene. The segmentation procedure is combined with a temporal tracking of the deformable structure over long image sequences, using a Kalman filtering approach. This combined segmentationtracking procedure has produced reliable extraction of deformable parts from long image sequences in adverse situations such as low signal-to-noise ratio, nongaussian noise, partial occlusions, or random initialization. The approach is demonstrated on a variety of synthetic as well as real-world image sequences featuring different classes of deformable objects

Citations