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4 - Fast correlation and phase correlation

from PART II - Similarity Metrics for Image Registration

Published online by Cambridge University Press:  03 May 2011

By
Harold S. Stone
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Harold S. Stone
Affiliation:
NEC Research Laboratory Retiree, New Jersey
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

Correlation is an extremely powerful technique for finding similarities between two images. This chapter describes why correlation has proved to be a valuable tool, how to implement correlation to achieve extremely high performance processing, and indicates the limits of correlation so that it can be used where it is appropriate. Section 4.1 gives the underlying theory for fast correlation, which is the well-known convolution theorem. It is this theory that gives correlation a huge processing advantage in many applications. Also covered is normalized correlation, which is a form of correlation that allows images to be matched in spite of differences in the images due to uniform changes of intensity. Section 4.2 treats the practical implementation of correlation, including the use of masks to eliminate irrelevant or obscured portions of images. The implementations described in this section treat images that differ only by translation, and otherwise have the same orientation and scale. Section 4.3 introduces an extension of the basic algorithm to allow for small differences of scale and orientation as well as translation. Section 4.4 presents very high precision registration. This section shows how to make use of Fourier phase to determine translational differences down to a few hundredths of a pixel. The images must be oriented and scaled identically and have a translational difference that does not exceed half of a pixel. Section 4.5 deals with fast rotational registration that uses phase correlation to discover the rotational difference between two images.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 79 - 111
Publisher: Cambridge University Press
Print publication year: 2011

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