Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Jón A. Benediktsson
- Acknowledgements
- PART I The Importance of Image Registration for Remote Sensing
- PART II Similarity Metrics for Image Registration
- PART III Feature Matching and Strategies for Image Registration
- 7 Registration of multiview images
- 8 New approaches to robust, point-based image registration
- 9 Condition theory for image registration and post-registration error estimation
- 10 Feature-based image to image registration
- 11 On the use of wavelets for image registration
- 12 Gradient descent approaches to image registration
- 13 Bounding the performance of image registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
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
Book contents
- Frontmatter
- Contents
- List of contributors
- Foreword by Jón A. Benediktsson
- Acknowledgements
- PART I The Importance of Image Registration for Remote Sensing
- PART II Similarity Metrics for Image Registration
- PART III Feature Matching and Strategies for Image Registration
- 7 Registration of multiview images
- 8 New approaches to robust, point-based image registration
- 9 Condition theory for image registration and post-registration error estimation
- 10 Feature-based image to image registration
- 11 On the use of wavelets for image registration
- 12 Gradient descent approaches to image registration
- 13 Bounding the performance of image registration
- PART IV Applications and Operational Systems
- PART V Conclusion
- Index
- Plate section
- Plate section
- References
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.
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- Image Registration for Remote Sensing , pp. 179 - 199Publisher: Cambridge University PressPrint publication year: 2011
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