1–8 von 8 Ergebnissen
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    Topology free hidden Markov models: application to background modeling

    Stenger, B. / Ramesh, V. / Paragios, N. et al. | IEEE | 2001
    from video, and illumination modeling. Their use involves an off-line learning step that is used as a basis for on-line decision making (i.e. a ...

      Topology Free Hidden Markov Models: Application to Background Modeling

      Stenger, B. / Ramesh, V. / Paragios, N. et al. | British Library Conference Proceedings | 2001

    Scene modeling and change detection in dynamic scenes: A subspace approach

    Mittal, A. / Monnet, A. / Paragios, N. | British Library Online Contents | 2009

    Random Exploration of the Procedural Space for Single-View 3D Modeling of Buildings

    Simon, L. / Teboul, O. / Koutsourakis, P. et al. | British Library Online Contents | 2011

    Motion-based background subtraction using adaptive kernel density estimation

    Mittal, A. / Paragios, N. | IEEE | 2004
    Background modeling is an important component of many vision systems. Existing work in the area has mostly addressed scenes that consist of ...

    Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

    Wang, C. / Komodakis, N. / Paragios, N. | British Library Online Contents | 2013

    Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications

    Ser-Nam Lim, / Mittal, A. / Davis, L.S. et al. | IEEE | 2005
    Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video ...

    Background Modeling and Subtraction of Dynamic Scenes

    Monnet, A. / Mittal, A. / Paragios, N. et al. | British Library Conference Proceedings | 2003

    A MRF-based approach for real-time subway monitoring

    Paragios, N. / Ramesh, V. | IEEE | 2001
    using a discontinuity preserving MRF-based approach where the information from different sources (background subtraction, intensity modeling) is ...