Selective perception sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from selecting the best scene locations, resolution, and vision operators, where `best' is defined as some function of benefit and cost (typically, their ratio or difference). Selective vision implies knowledge about the scene domain and the imaging operators. We use Bayes nets for representation and benefit-cost analysis in a selective vision system with both visual and non-visual actions in real and simulated static and dynamic environments. We describe sensor fusion, dynamic scene, and multi-task applications.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bayes nets for selective perception and data fusion


    Beteiligte:

    Kongress:

    23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities ; 1994 ; Washington,DC,United States


    Erschienen in:

    Erscheinungsdatum :

    1995-01-31





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Control of Selective Perception Using Bayes Nets and Decision Theory

    Rimey, R. D. / Brown, C. M. | British Library Online Contents | 1994


    Dynamic Bayes Nets in SHM

    Bartram, G. / Mahadevan, S. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2012


    Dynamic Bayes Nets in SHM

    Bartram, Gregory / Mahadevan, Sankaran | AIAA | 2012



    On the Bayes fusion of visual features

    Shi, X. / Manduchi, R. | British Library Online Contents | 2007