Abstract
A planar shape-recognition approach is presented which is based on
hidden Markov models 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 much shape contour perturbation, and a
moderate amount of occlusion. 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
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