The robust regression techniques in the RANSAC family are popular today in computer vision, but their performance depends on a user supplied threshold. We eliminate this drawback of RANSAC by reformulating another robust method, the M-estimator, as a projection pursuit optimization problem. The projection based pbM-estimator automatically derives the threshold from univariate kernel density estimates. Nevertheless, the performance of the pbM-estimator equals or exceeds that of RANSAC techniques tuned to the optimal threshold, a value which is never available in practice. Experiments were performed both with synthetic and real data in the affine motion and fundamental matrix estimation tasks.


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    Titel :

    Robust regression with projection based M-estimators


    Beteiligte:
    Haifeng Chen, (Autor:in) / Meer, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    697476 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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