Shape classification using hidden Markov model

A planar shape recognition approach is presented which is based on a hidden Markov model and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments to make classification at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. Also, the overall classification scheme is independent of shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained all over again when a new class of shapes is added