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.


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    Title :

    A robust interest points matching algorithm


    Contributors:
    Il-Kyun Jung, (author) / Lacroix, S. (author)


    Publication date :

    2001-01-01


    Size :

    880412 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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