We describe an integrated vision system which reliably detects persons in static color natural scenes, or other targets among distracting objects. The system is built upon the biologically-inspired synergy between two processing stages: A fast trainable visual attention front-end (where), which rapidly selects a restricted number of conspicuous image locations, and a computationally expensive object recognition back-end (what), which determines whether the selected locations are targets of interest. We experiment with two recognition back-ends: One uses a support vector machine algorithm and achieves highly reliable recognition of pedestrians in natural scenes, but is not particularly biologically plausible, while the other is directly inspired from the neurobiology of inferotemporal cortex, but is not yet as robust with natural images. Integrating the attention and recognition algorithms yields substantial speedup over exhaustive search, while preserving detection rate. The success of this approach demonstrates that using a biological attention-based strategy to guide an object recognition system may represent an efficient strategy for rapid scene analysis.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Neuromorphic algorithms for computer vision and attention


    Beteiligte:

    Kongress:

    Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV ; 2001 ; San Diego,CA,United States


    Erschienen in:

    Erscheinungsdatum :

    2001-11-14





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Neuromorphic vision processing system

    Lin / Chen / Chiueh | IET Digital Library Archive | 1997


    Feedback control of dynamical systems using neuromorphic vision sensors

    Mueller, Erich, Ph. D. Massachusetts Institute of Technology | DSpace@MIT | 2016

    Freier Zugriff

    Analysing Driver's Attention Level using Computer Vision

    Bergasa, Luis M. / Buenaposada, Jose M. / Nuevo, Jesus et al. | IEEE | 2008


    Finding the gap: neuromorphic motion-vision in dense environments

    Schoepe, Thorben / Janotte, Ella / Milde, Moritz B. et al. | DataCite | 2024


    Performance of Computer Vision Algorithms

    Weng, J. / Huang, T. S. | British Library Online Contents | 1994