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Proceedings Paper

New insights into correlation-based template matching
Author(s): James Ooi; Kashi Rao
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Paper Abstract

Correction-based template matching has been used extensively in computer vision for object recognition and also for other tasks such as edge detection, stereo, motion and inspection. It has also found wide application in character recognition. A deeper understanding of the performance of this technique for such tasks would help predict when it will succeed or fail. Previous work on this problem has examined correlation-based template matching using signal processing techniques. Our approach is different: we dissect it employing concepts from geometry and physics. This leads to new insights into correlation-based template matching. We study the performance of correlation between images for different lighting conditions, viewpoints and scales of a scene, obtaining new results for scale variation and viewpoint change for binary images. We analyze gray level images for changes in lighting alone and obtain useful and novel formulae. Knowing how correlation behaves with these changes helps to strategically distribute templates for a given recognition task. We then develop a method to compute the probability of confusion for recognition by template matching. We obtain a closed form solution for the probability of confusion in the two template case. We conclude by noting that template matching encounters difficulties in tasks such as object recognition because of its strong dependence on viewing conditions, although it can be useful in some situations when templates are chosen and positioned judiciously.

Paper Details

Date Published: 1 March 1991
PDF: 12 pages
Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); doi: 10.1117/12.28670
Show Author Affiliations
James Ooi, Texas Instruments Inc. (United States)
Kashi Rao, Texas Instruments Inc. (United States)

Published in SPIE Proceedings Vol. 1468:
Applications of Artificial Intelligence IX
Mohan M. Trivedi, Editor(s)

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