Shape recognition using a nonstationary autoregressive hidden Markov model

An autoregressive hidden Markov model (ARHMM) is introduced for the analysis and classification of shape boundaries. The principal features of this model are: an autoregressive shape representation that is invariant to scaling, rotation and translation; a nonstationary contour characterization providing descriptions of abrupt and gradual changes in complex boundaries typical in image analysis; and a hidden Markov model (HMM) for description of such changes. An experimental study is presented which demonstrates the model´s effectiveness