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8 - New approaches to robust, point-based image registration

from PART III - Feature Matching and Strategies for Image Registration

Published online by Cambridge University Press:  03 May 2011

By
David M. Mount ,
Nathan S. Netanyahu  and
San Ratanasanya
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
David M. Mount
Affiliation:
University of Maryland, Maryland
Nathan S. Netanyahu
Affiliation:
University of Maryland, Maryland
San Ratanasanya
Affiliation:
University of Maryland, Maryland
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

We consider various algorithmic solutions to image registration based on the alignment of a set of feature points. We present a number of enhancements to a branch-and-bound algorithm introduced by Mount, Netanyahu, and Le Moigne (Pattern Recognition, Vol. 32, 1999, pp. 17–38), which presented a registration algorithm based on the partial Hausdorff distance. Our enhancements include a new distance measure, the discrete Gaussian mismatch, and a number of improvements and extensions to the above search algorithm. Both distance measures are robust to the presence of outliers, that is, data points from either set that do not match any point of the other set. We present experimental studies, which show that the new distance measure considered can provide significant improvements over the partial Hausdorff distance in instances where the number of outliers is not known in advance. These experiments also show that our other algorithmic improvements can offer tangible improvements. We demonstrate the algorithm's efficacy by considering images involving different sensors and different spectral bands, both in a traditional framework and in a multiresolution framework.

Introduction

Image registration involves the alignment of two images, called the reference image and the input image, taken of the same scene. The objective is to determine the transformation from some given geometric group that most nearly aligns the input image with the reference image.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 179 - 199
Publisher: Cambridge University Press
Print publication year: 2011

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