Performance Bound of Position Estimation in Image Matching

Abstract:

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Position estimation in image matching is a fundamental step in computer vision and image processing. To deal with the problem of performance prediction, we formulate it under statistical parameter estimation aspect. The lower bound of position estimation variance is obtained based on Cramer-Rao lower bound (CRLB) theory. This paper analyses the impact of noise to 1-D signal matching, derives the lower bound of variance, and then extends it to 2-D image matching. Furthermore, we derive numerical expression that can be computed from observed data. Finally, we use Monte Carlo simulation method to verify the derived analytical expressions. Experimental results show that the derived CRLB is tight to simulation estimated variance. The CRLB can characterize the performance bound of position estimation in image matching.

Info:

Periodical:

Edited by:

David Wang

Pages:

766-772

DOI:

10.4028/www.scientific.net/KEM.500.766

Citation:

B. S. Xu and Z. L. Shi, "Performance Bound of Position Estimation in Image Matching", Key Engineering Materials, Vol. 500, pp. 766-772, 2012

Online since:

January 2012

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$35.00

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