Reformulating the Costeira-Kanade algorithm as a pure mathematical theorem independent of the Tomasi-Kanade factorization, we present a robust segmentation algorithm by incorporating such techniques as dimension correction, model selection using the geometric AIC, and least-median fitting. Doing numerical simulations, we demonstrate that oar algorithm dramatically outperforms existing methods. It does not involve any parameters which need to be adjusted empirically.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Motion segmentation by subspace separation and model selection


    Beteiligte:
    Kanatani, K. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    649147 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Motion Segmentation by Subspace Separation and Model Selection

    Kanatani, K. / IEEE | British Library Conference Proceedings | 2001


    Illumination Subspace for Multibody Motion Segmentation

    Maki, A. / Hattori, H. / IEEE | British Library Conference Proceedings | 2001


    Photometric subspace for multibody motion segmentation

    Maki, A. | British Library Online Contents | 2004



    Motion analysis: model selection and motion segmentation

    Gheissari, N. / Bab-Hadiashar, A. | IEEE | 2003