2-D Shape Classification Using Hidden Markov Model

  • Authors:
  • Yang He;Amlan Kundu

  • Affiliations:
  • -;-

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 1991

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Abstract

The authors present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters. This approach segments closed shapes to make classifications at a finer level. The algorithm can tolerate a lot of shape contour perturbation and a moderate amount of occlusion. An orientation scheme is described to make the overall classification insensitive to shape orientation. Excellent recognition results have been reported. A distinct advantage of the approach is that the classifier does not have to be trained again when a new class of shapes is added.