Vision tasks, such as motion analysis, object tracking, robot localization, and 3D modeling, often require the fusion of estimates coming from different sources. Most of the fusion algorithms, however, are not robust with respect to outliers and only consider one source models. Their performance deteriorates when initial assumptions are not valid (e.g., the presence of outliers in the data or data corresponding to multiple motions). The paper presents a statistical solution to the fusion problem based on variable-bandwidth kernel density estimation. The fusion estimate is represented by the mode of a density function that exploits the uncertainty of the estimates to be fused. We show that the fusion estimate is consistent and conservative. Since our construction is founded on density estimation, it handles naturally outliers and multiple source models. We test the density-based fusion for the task of multiple motion computation. Superior experimental results validate our theory.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Density estimation-based information fusion for multiple motion computation


    Beteiligte:
    Comaniciu, D. (Autor:in)


    Erscheinungsdatum :

    01.01.2002


    Format / Umfang :

    1208501 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Density Estimation-Based Information Fusion for Multiple Motion Computation

    Comaniciu, D. | British Library Conference Proceedings | 2002



    Nonparametric Information Fusion for Motion Estimation

    Comaniciu, D. / IEEE | British Library Conference Proceedings | 2003



    Data Fusion-Based Traffic Density Estimation and Prediction

    Anand, Asha / Ramadurai, Gitakrishnan / Vanajakshi, Lelitha | Taylor & Francis Verlag | 2014