This work investigates the image registration from feature point sets. Image registration is a fundamental object recognition method in computer vision and aims to find best matches between two or more point sets when there are geometric distortions, point measurement errors and contamination present. This paper concentrates on image registration from feature point sets whose transformation is affme or projective and gives closed form solutions of the transformation parameters, respectively. Furthermore, the Random Sample Consensus algorithm is applied to the matching problem in order to yield the transformation, as well as a set of points in correspondence from images directly. The experimental results show that the methods are accurate, stable and are affected slightly by noise.
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