A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes

被引:39
作者
Kervrann, C
Heitz, F
机构
[1] Inst Natl Rech Informat & Automat, IRISA, F-35042 Rennes, France
[2] CNRS, URA 1871, LSHT, ENSPS, F-67400 Illkirch Graffenstaden, France
来源
GRAPHICAL MODELS AND IMAGE PROCESSING | 1998年 / 60卷 / 03期
关键词
deformable models; Markov models; image sequence analysis; segmentation; tracking; Kalman filtering;
D O I
10.1006/gmip.1998.0469
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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, (C) 1998 Academic Press.
引用
收藏
页码:173 / 195
页数:23
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