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13 - Bounding the performance of image registration

from PART III - Feature Matching and Strategies for Image Registration

Published online by Cambridge University Press:  03 May 2011

By
Min Xu  and
Pramod K. Varshney
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Min Xu
Affiliation:
Syracuse University, New York
Pramod K. Varshney
Affiliation:
Syracuse University, New York
Jacqueline Le Moigne
Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu
Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman
Affiliation:
Loyola University Maryland
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Summary

Abstract

Performance bounds can be used as a performance benchmark for any image registration approach. These bounds provide insights into the accuracy limits that a registration algorithm can achieve from a statistical point of view, that is, they indicate the best achievable performance of image registration algorithms. In this chapter, we present the Cramér-Rao lower bounds (CRLBs) for a wide variety of transformation models, including translation, rotation, rigid-body, and affine transformations. Illustrative examples are presented to examine the performance of the registration algorithms with respect to the corresponding bounds.

Introduction

Image registration is a crucial step in all image analysis tasks in which the final information is obtained from the combination of various data sources, as in image fusion, change detection, multichannel image restoration, and object recognition. See, for example, Brown (1992) and Zitová and Flusser (2003). The accuracy of image registration affects the performance of image fusion or change detection in applications involving multiple imaging sensors. For example, the effect of registration errors on the accuracy of change detection has been investigated by Townshend et al. (1992), Dai and Khorram (1998), and Sundaresan et al. (2007). An accurate and robust image registration algorithm is, therefore, highly desirable.

The purpose of image registration is to find the transformation parameters, so that the two given images that represent the same scene are aligned. There are many factors that might affect the performance of registration algorithms.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 276 - 290
Publisher: Cambridge University Press
Print publication year: 2011

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References

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