1–8 of 8 hits
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    Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

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

    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

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

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

    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 ...

    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 ...

    Background Modeling and Subtraction of Dynamic Scenes

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

    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

    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 ...