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Proceedings Paper

Maximum Likelihood Image Registration With Subpixel Accuracy
Author(s): Michael S Mort; M. D. Srinath
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Paper Abstract

The problem addressed in this paper is to estimate, with an error which is substantially less than the dimensions of a pixel, the unknown displacement d between two images of a common scene, given only the image data. Assuming a Gauss-Markov model for the scene, the joint probability density function of the two images is obtained and an implicit expression for the maximum likelihood estimate of the displacement is found as the maximum of a functional J(c1). The sensitivity of the algorithm to the model parameters has been determined by experiments on 32 real images. The experiments show that a mean absolute error of 1/20 of a pixel dimension is achievable for rms signal to rms noise ratios down to a value of 5.

Paper Details

Date Published: 16 December 1988
PDF: 8 pages
Proc. SPIE 0974, Applications of Digital Image Processing XI, (16 December 1988); doi: 10.1117/12.948429
Show Author Affiliations
Michael S Mort, Signal Analytics (United States)
M. D. Srinath, Southern Methodist University (United States)

Published in SPIE Proceedings Vol. 0974:
Applications of Digital Image Processing XI
Andrew G. Tescher, Editor(s)

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