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Publications

  1. C. Corridori; D. Giordani; P. Lombardi; S. Messelodi; C. M. Modena; M. Zanin,
    An in-vehicle vision system for dangerous situation detection,
    Proceedings,
    Perugia,
    Morlacchi Editore,
    2004
    , (Conferenza Italiana sui Sistemi Intelligenti 2004 – 2° Convegno del Gruppo Italiano Ricercatori in Pattern Recognition GIRPR-04,
    Perugia, Italy,
    from 09/15/2004 to 09/15/2004)
  2. C. Andreatta; M. Lecca; S. Messelodi,
    MEMORI,
    RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
    2004
    , (RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
    26/04/2004)
  3. Qi. Xu; R. Brunelli; S. Messelodi; J. Zhang; M. Li,
    Image Coherence Based Adaptive Sampling for Image Synthesis,
    Computational Science and its Applications - ICCSA 2004,
    Springer,
    vol.3044/2004,
    2004
    , pp. 693-
    702
    , (International Conference on Computational Science and its Applications - ICCSA 2004,
    Assisi, Italy,
    05/2004)
  4. O. Lanz,
    Automatic Lens Distortion Estimation for an Active Camera,
    2004
    , pp. 575-
    580
    , (International Conference on Computer Vision and Graphics - ICCVG,
    Warsaw (Poland),
    da 09/22/2004 a 09/24/2004)
  5. O. Lanz,
    Occlusion Robust Tracking of Multiple Objects,
    Computational Imaging and Vision,
    Springer,
    vol.32,
    2004
    , pp. 715-
    720
    , (International Conference on Computer Vision and Graphics - ICCVG,
    Warsaw (Poland),
    from 09/22/2004 to 09/24/2004)
  6. Q. Xu; L. Ma; M. Li; W. Wang; J. Cai; R. Brunelli; S. Messelodi,
    Fuzzy weighted average filtering for mixture noises,
    2004
    , pp. 18-
    21
    , (Third International Conference on Image and Graphics (ICIG'04),
    Hong Kong, China,
    12/18/2004 - 12/20/2004)
  7. M. Ciappa; M. Stangoni; W. Fichtner; A. Scorzoni; E. Ricci,
    On the Use of Neural Networks to Solve the Reverse Modelling Problem for the Quantification of Dopant Profiles Extracted by Scanning Probe Microscopy Techniques,
    2004
    , pp. 1703-
    1708
    , (5th European symposium reliability of electron devices, failure physics and analysis - ESREF 2004,
    Zuerich, Switzerland,
    from 10/04/2004 to 10/08/2004)
  8. R. Brunelli,
    Computer Vision and Graphics,
    vol.32,
    2004
    , pp. 962-
    967
    , (International Conference on Computer Vision and Graphics - ICCVG,
    Warsaw, Poland,
    from 09/22/2004 to 09/24/2004)
  9. O. Lanz,
    Modeling Interactions in Multiple Object Bayesian Tracking,
    This paper proposes a framework for modeling interactions in muliple object 3D Bayesian tracking. It exploits both the computational cheapness of independent single object filters and the modeling power of the joint filter. Sampling from complex joint propagation densities is avoided by introducing interaction a posteriori, after blind single object propagation has been performed. To deal with occlusions for each object a support layer is computed. It contains probabilistic information about how likely a pixel is occluded by another object. Utilized to give less weight to likely occluded pixels, it provides the basis of a robust likelihood model. The implementation of the proposed ideas in a Sequential Monte Carlo framework are discussed. Experiments on a synthetic data show the robustness of the proposed ideas,
    2004
  10. S. Messelodi; C. M. Modena; M. Zanin,
    A computer vision system for the detection and classification of vehicles at urban road intersections,
    This paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences from pole-mounted video cameras at urban crossroads.
    The system is flexible with respect to road geometry and camera position, permitting data collection from several monitored crosses. It uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing background subtraction and a feature-based tracking methodology. For each detected vehicle, the system is able to describe its path, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one.
    Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a one hundred thousand people town in northern Italy
    ,
    2004

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