2-D shape classification using hidden Markov model
IEEE Transactions on Pattern Analysis and Machine IntelligencePublished 1 January 1991
Yulin He, A. Kundu
Citations165
SJR quartileQ1
SJR score3.91
SNIP5.99
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TL;DR
The authors present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters that segments closed shapes to make classifications at a finer level and does not have to be trained again when a new class of shapes is added.
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.>
Keywords
Computer Science
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