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Proceedings Paper

Improved distortion-invariant pattern recognition through synthesizing similar training images into a composite image
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Paper Abstract

In this work, a distortion-invariant pattern recognition scheme called the composite training image method is introduced. Usually, in attempting to detect the distorted (rotated, size- changed, shifted) versions of an object, a large number of raw training (distorted) images are used. However, there is a trade-off between this number and the ratio of signal correlation intensity peak to the maximum sidelobe (RSMS). In order not to degrade this ratio, the number of training images should be reduced as much as possible. We show how to fuse several similar raw training images into a composite training image. In this paper, we illustrate the feasibility of using such composite training images.

Paper Details

Date Published: 30 October 1992
PDF: 8 pages
Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); doi: 10.1117/12.131216
Show Author Affiliations
Chulung Chen, Carnegie Mellon Univ. (Taiwan)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 1812:
Optical Computing and Neural Networks
Ken Yuh Hsu; Hua-Kuang Liu, Editor(s)

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