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2-D Shape Classification Using Hidden Markov Model
November 1991 (vol. 13 no. 11)
pp. 1172-1184

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.

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Index Terms:
pattern recognition; segmentation; 2-D shape classification; hidden Markov model; planar shape recognition; autoregressive parameters; shape contour perturbation; occlusion; orientation scheme; shape orientation; Markov processes; pattern recognition
Citation:
Y. He, A. Kundu, "2-D Shape Classification Using Hidden Markov Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 11, pp. 1172-1184, Nov. 1991, doi:10.1109/34.103276
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