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This paper addresses the problem of aligning the images of feet taken at different instances in time. We propose to use SIFT keypoints to find the geometric deformation between two photo’s. We then have a set of landmarks for each image. By finding the corresponding landmarks (i.e. matching the keypoints), we know the deformation in a sparse set of points. Using these points, we calculate a B-spline deformation model, which allows us to describe the deformation in every point in the image. The algorithm that we have developed matches the keypoints, and updates the deformation model iteratively from coarse to fine. This significantly increases the performance in terms of speed and robustness. In order to obtain enough reliable keypoints, we propose a debayering algorithm to realize a relatively high resolution in the green channel. In addition we apply processing to the image to enhance its local contrast.

This paper addresses the problem of aligning the images of feet taken at different instances in time. We propose to use SIFT keypoints to find the geometric deformation between two photo’s. We then have a set of landmarks for each image. By finding the corresponding landmarks (i.e. matching the keypoints), we know the deformation in a sparse set of points. Using these points, we calculate a B-spline deformation model, which allows us to describe the deformation in every point in the image. The algorithm that we have developed matches the keypoints, and updates the deformation model iteratively from coarse to fine. This significantly increases the performance in terms of speed and robustness. In order to obtain enough reliable keypoints, we propose a debayering algorithm to realize a relatively high resolution in the green channel. In addition we apply processing to the image to enhance its local contrast.