2-D SHAPE CLASSIFICATION USING HIDDEN MARKOV MODEL

被引：101

作者：

HE, Y

KUNDU, A

机构：

[1] Department of Electrical and Computer Engineering, State University of New York at Buffalo, Amherst, NY

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1991年 / 13卷 / 11期

关键词：

AUTOREGRESSIVE MODEL; HIDDEN MARKOV MODEL; NONSTATIONARY TRANSITION; PATTERN RECOGNITION; SEGMENTAL K-MEANS ALGORITHM; SHAPE CLASSIFICATION; SHAPE OCCLUSION; SHAPE ORIENTATION; STATIONARITY TEST;

D O I：

10.1109/34.103276

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a planar shape recognition approach based on the hidden Markov model and autoregressive parameters. This approach segments closed shapes into segments and explores the characteristic relations between consecutive segments 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.

引用

页码：1172 / 1184

页数：13

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