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The authors propose a novel image registration framework which uses classifiers trained from examples of aligned images to achieve registration. The approach is designed to register images of medical data where the physical condition of the patient has changed significantly and image intensities are drastically different. They use two boosted classifiers for each degree of freedom of image transformation. These two classifiers can both identify when two images are correctly aligned and provide an efficient means of moving towards correct registration for misaligned images.
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