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Stefano Messelodi

Head of Unit
  • Phone: 0461314509
  • FBK Povo
Short bio

Stefano Messelodi was born in Arco (Italy) in 1961. He graduated in computer science from the University of Milan (Italy).  Since 1986 he is working in FBK (ITC-irst), Trento, Italy, where he coordinated the Technologies of Vision research unit till 2012. His research interests include text localization in scene, semantic image labelling and dynamic scene understanding. He served as a reviewer for several journals and conferences. He is a member of IEEE Society and International Association for Pattern Recognition.
He currently coordinates the research unit TeV.

Research interests
people re-identification text localization in scene document image analysis traffic analysis
Publications
  1. Stefano Messelodi,
    Video-based technologies for surveillance and monitoring,
    2008
    , pp. 6-
    9
    , (Workshop on Video surveillance projects in Italy - VISIT 2008,
    Modena, Italy,
    22/05/2008)
  2. Michela Lecca; Stefano Messelodi,
    Estimating Illuminant Changes in Color Images by Color Histogram Comparison,
    The colors of a scene are strongly dependent on the light source. For this reason, the systems for image retrieval or object recognition based on the analysis of descriptors related to the colors, cannot work well when illuminant changes occur. This work proposes a novel method for estimating the changes of illuminants occurring between the images of a same scene captured under different photometric conditions. The illuminant variations are represented by the von Kries diagonal model and their estimate is performed by minimizing a dissimilarity measure between the color histograms of the images under examination. A technique for illuminant invariant image and object identification based on the estimates of the light changes is also presented, along with a comparison with other approaches.,
    2008
  3. Michela Lecca; Stefano Messelodi,
    Estimating Illuminant Changes by Piecewise Inversion of Cumulative Color Histograms,
    We present a new linear algorithm for the computation of the illuminant change occurring between two color images. We approximate the light variations by the von Kries diagonal transform, whose coefficients we estimate by minimizing a dissimilarity measure between the piecewise inversions of the cumulative color histograms of the considered images. We provide an analysis about the accuracy of our estimate and we explain how to use it for illuminant invariant image retrieval.,
    2008
  4. Stefano Messelodi; Carla Maria Modena; Gianni Cattoni,
    in «PATTERN RECOGNITION LETTERS»,
    vol. 28,
    n. 13,
    2007
    , pp. 1719 -
    1726
  5. Michela Lecca; Stefano Messelodi,
    18th British Machine Vision Conference,
    Coventry,
    vol.1,
    2007
    , pp. 112-
    121
    , (18th British Machine Vision Conference,
    Coventry, United Kingdom,
    10/09/2007 - 13/09/2007)
  6. M. Lecca; S. Messelodi; C. Andreatta,
    An Object Recognition System for Automatic Image Annotation and Browsing of Object Catalogs,
    New York,
    ACM,
    2007
    , pp. 154-
    155
    , (15th International Conference on Multimedia - ACMMM,
    Augsburg, Germany,
    from 09/24/2007 to 09/29/20007)
  7. Stefano Messelodi; Carla Maria Modena; Michele Zanin; Fabrizio Granelli; Francesco De Natale; Enrico Betterle; Andrea Guarise,
    Extended Traffic Data Collection by Intelligent Vehicles,
    The elaboration of floating car data, i.e. data collected by vehicles moving on road network, is relevant for traffic management and for private service providers, which can bundle updated traffic information with navigation services. Floating data, in its extended acceptation, contains not only time and location provided by an on-board positioning system, but also information coming from othervarious vehicle sensors.In this report we describe our extended data collection system, in which vehicles are able to collect data about their local environment, namely the presence of roadworks and traffic slowdowns, by analyzing visual data taken by a looking forward camera and data from the on-board Electronic Control Unit. Upon detection of such events, apacket is set up containing time, position, vehicle data, results of on-board elaboration, one or more images of the road ahead and an estimation of the local traffic level. Otherwise, the transmitted packet containing only the minimal data, making its size adaptive to the environment surrounding the vehicle.,
    2007
  8. M. Lecca; S. Messelodi,
    Recognition and Reconstruction of Partially Occluded Objects,
    XVI International Conference on Computer Science,
    World Enformatika Society,
    2006
    , pp. 233-
    238
    , (XVI International Conference on Computer Science,
    Venice, Italy,
    24/11/2006 - 26/11/2006)
  9. S. Messelodi; C. M. Modena; M. Zanin,
    in «PATTERN ANALYSIS AND APPLICATIONS»,
    vol. 8,
    n. 1-2,
    2005
    , pp. 17 -
    31
  10. S. Messelodi; C. M. Modena,
    in «ADVANCES IN TRANSPORTATION STUDIES»,
    vol. 7,
    n. b,
    2005
    , pp. 51 -
    66

Pages