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JRM Vol.15 No.3 pp. 278-285
doi: 10.20965/jrm.2003.p0278
(2003)

Paper:

Pattern Recognition by Hierarchical Feature Extraction

Daigo Misaki, Shigeru Aomura, and Noriyuki Aoyama

Graduate School of Engineering and Faculty of Engineering, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397 Japan

Received:
December 25, 2002
Accepted:
March 4, 2003
Published:
June 20, 2003
Keywords:
pattern recognition, DP mathching, local and global features
Abstract

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called “landmark”s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

Cite this article as:
Daigo Misaki, Shigeru Aomura, and Noriyuki Aoyama, “Pattern Recognition by Hierarchical Feature Extraction,” J. Robot. Mechatron., Vol.15, No.3, pp. 278-285, 2003.
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