A robust interest points matching algorithm

Abstract

This paper presents an algorithm that matches interest points detected on a pair of grey level images taken from 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 paints: 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.