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Proceedings Paper

Template matching based on rank-order operations
Author(s): Mehdi Khosravi; Ronald W. Schafer
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Paper Abstract

Linear correlation techniques are useful approaches for template matching. However, they are computationally intensive since large numbers of multiplications are involved in their calculation. This paper introduces a family of rank-order-based criteria (ROBC) which are multiplier free and do not depend on the local average of the image/template. The most primitive member of this family has properties analogous to the properties of the normalized linear correlation. Hence, we call it normalized min-max cross-correlation (NMCC). Experimental results are presented that describe the performance of the introduced criteria in the presence of Gaussian and impulsive noise. These experiments show that the NMCC features sharp and robust indications in the presence of Gaussian noise. Other members of the ROBC family with more rank order terms also are robust with respect to impulsive noise.

Paper Details

Date Published: 1 May 1994
PDF: 12 pages
Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); doi: 10.1117/12.172556
Show Author Affiliations
Mehdi Khosravi, Georgia Institute of Technology (United States)
Ronald W. Schafer, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2180:
Nonlinear Image Processing V
Edward R. Dougherty; Jaakko Astola; Harold G. Longbotham, Editor(s)

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