DEFORMABLE CONTOURS - MODELING AND EXTRACTION

This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images, We propose a global contour mod el based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts i nfluence over a global model while allowing for deformations, We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour mod el. We discuss pertinent issues in shape training, energy minimization , line search strategies, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.