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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    Titel :

    A robust interest points matching algorithm


    Beteiligte:
    Il-Kyun Jung, (Autor:in) / Lacroix, S. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    880412 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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