A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Distributed genetic algorithm for subtraction radiography
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Image Registration Based on Lifting Process: An Application to Digital Subtraction Radiography
IEEE Transactions on Information Technology in Biomedicine
Self-organizing nets for optimization
IEEE Transactions on Neural Networks
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In this paper, an Automatic Iterative Point Correspondence (AIPC) algorithm towards image registration is presented. Given an image pair, distinctive points are extracted only in one of the images (reference image), and the corresponding points in the other image are obtained automatically by maximizing a similarity measure between regions of the two images with respect to the parameters of a local transformation. The maximization is accomplished by means of an iterative procedure, in which candidate solutions for the transformation parameters are tested at each iteration; these solutions are evaluated by the similarity measure between image regions. The detected point pairs by the application of the AIPC algorithm are then used to estimate the parameters of a global projective transformation for the registration of the image pair. The proposed AIPC algorithm was applied on 113 in vitro and in vivo dental image pairs providing improved registration accuracy against three widely used registration methods.