A Hierarchical Markov Modeling Approach for the Segmentation and Tracking of Deformable Shapes

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• 作者：Kervrann ; Charles ; Heitz ; Fabrice
• 关键词：deformable models ; Markov models ; image sequence analysis ; segmentation ; tracking ; Kalman filtering
• 刊名：Graphical Models and Image Processing
• 出版年：1998
• 出版时间：May, 1998
• 年：1998
• 卷：60
• 期：3
• 页码：173-195
• 全文大小：1.87 M

文摘

In many applications of dynamic scene analysis, the objects or structures to be analyzed undergo deformations that have to be modeled. In 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 segmentation-tracking 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.