NOTE: The following software is provided for research purposes only. You may not distribute this software in any form without the prior approval of the University of California. 

An implementation of the contour-based algorithm is now available. The strategy and explanations can be found in a paper  "A contour-based approach to multisensor image registration", IEEE Transactions on Image Processing, vol.4, (no.3), pp.320-34, March 1995. [abstract]

Objectives

Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral and multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. In this paper, we present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, we propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of our algorithms, which have outperformed manual registration in terms of root mean square error at the control points.

Acknowledgements:

The following students contributed to the code development and/or testing: David Garza, Yining Deng, and Alan Trombla.

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