Digital Image Processing
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
Multimodal Registration using the Discrete Wavelet Frame Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
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The aim of this article is to introduce a computationally tractable mathematical model of the relation between the complex wavelet coefficients of two different images of the same scene. Because the two images are acquisitioned at distinct times, from distinct viewpoints, or by distinct sensors, the relation between the wavelet coefficients is far too complex to handle it in a deterministic fashion. This is why we consider adequate and present a probabilistic model for this relation. We further integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has subpixel accuracy, and is robust to noise and to a large class of local variations like changes in illumination and even occlusions. We empirically prove the properties of this algorithm using synthetic and real data.