A Supervised Approach to Deformable Contour Tracking
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چکیده
Contour tracking is a tough problem since the motion of the contour exhibits a complex dynamic process in which nonlinear deformation often occurs. This paper proposes a supervised approach to deformable contour tracking. The tracking system is built on a dynamic Bayesian network, which introduces one more hidden Markov process to switch among different contours. The contours are used to help the tracker to estimate and predict the target’s position through motion decomposition. The motion is decomposed into two components: a global affine motion and a kind of local nonrigid deformation. The global affine motion and the local deformation are utilized together to limit the sample space and supervise the sampling process by combining with the information from image observations. This approach has an advantage that only a small number of samples are needed to infer the motion state during tracking. The effectiveness of the proposed method for tracking deformable contour is demonstrated for a variety of image sequences.
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