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

Chulung Chen; Bhagavatula Vijaya Kumar

doi:10.1117/12.131216

30 October 1992 Improved distortion-invariant pattern recognition through synthesizing similar training images into a composite image

Chulung Chen, Bhagavatula Vijaya Kumar

Author Affiliations +

Proceedings Volume 1812, Optical Computing and Neural Networks; (1992) https://doi.org/10.1117/12.131216
Event: International Symposium on Optoelectronics in Computers, Communications, and Control, 1992, Hsinchu, Taiwan

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.

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Chulung Chen and Bhagavatula Vijaya Kumar "Improved distortion-invariant pattern recognition through synthesizing similar training images into a composite image", Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); https://doi.org/10.1117/12.131216

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