A robust interest points matching algorithm

This paper presents an algorithm that matches interest points detected on a pair of grey level images taken front arbitrary points of view First matching hypotheses are generated using a similarity measure of the interest points. Hypotheses are confirmed using local groups of interest points: group matches are based on a measure defined on an affine transformation estimate and on a correlation coefficient computed on the intensity of the interest points. Once a reliable match has been determined for a given interest point and the corresponding local group, new group matches are found by propagating the estimated affine transformation. The algorithm has been widely tested under various image transformations. it provides dense matches and is very robust to outliers, i.e. interest points generated by noise or present in only one image because of occlusions or non overlap.