A Robust Interest Points Matching Algorithm

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 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.

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Il-Kyun Jung, Simon Lacroix
• Keywords: Affine Transformation Estimate, Arbitrary Points, Computer Vision, Corresponding Local Group, Dense Matches, ICCV 2001