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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
  1. Michela Lecca; Stefano Messelodi,
    Planckian Illuminants and Von Kries Model,
    Technical Report -

    In this work the authors investigate two hypotheses commonly assumed in Computer Vision and Computer Graphics for solvin

    In this work the authors investigate the relations between the colors of two pictures of a same scene taken under two different light sources. This is a crucial task in Computer Vision and Computer Graphics.
    The authors show that: (1) the von Kries model suffices for describing the color variation due to a Planckian illuminant change; (2) the Planck's law constraints the triplets of the von Kries coefficients to be points of a ruled surfaces parametrized by the photometric cues of the illuminants; (3) the von Kries coefficients are strongly related to the sensitivity functions of the device.
    Several experiments carried out on public image datasets are presented.

  2. C. M. Modena; S. Messelodi,
    Recognition and tracking of embedded text in sport videos: a case study,
    Abstract - We present an athlete recognition module designed for broadcast videos, forming part of a system designed for the personalization of sport video broadcast. The aim of this module is the identification of athletes in the scene through the reading of names or numbers printed on their uniforms and to identify frames where athletes are visible. Using an adaptation of a previously published algorithm we extract text from individual frames and then read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to an a-priori list of athletes' names (or numbers), to provide a presence score for each athlete. Text regions are subsequently tracked in following frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, can also be exploited to provide a denser labelling of the video sequences.,
  3. Stefano Messelodi; Carla Maria Modena; Michele Zanin; Francesco De Natale; Fabrizio Granelli; Enrico Betterle; Andrea Guarise,
    vol. 36,
    n. 3 Part 1,
    , pp. 4213 -
  4. Michela Lecca; Stefano Messelodi,
    Computing von Kries Illuminant Changes by Piecewise Inversion of Cumulative Color Histograms,
    , pp. 1 -
  5. Michela Lecca; Stefano Messelodi,
    Illuminant Change Estimation via Minimization of Color Histogram Divergence,
    Computational Color Imaging Workshop,
    n. LNCS 5646,
    , pp. 41-
    , (Computational Color Imaging Workshop,
    Saint-Etienne, France,
    03/26/2009 - 03/27/2009)
  6. O. Lanz; S. Messelodi,
    A sampling algorithm for occlusion robust multi target detection,
    , pp. 346-
    , (6th IEEE International Conference on Advanced Video and Signal based Surveillance - AVSS 2009,
    Genova, Italy,
    da 09/02/2009 a 09/04/2009)
  7. Stefano Messelodi,
    Video-based technologies for surveillance and monitoring,
    , pp. 6-
    , (Workshop on Video surveillance projects in Italy - VISIT 2008,
    Modena, Italy,
  8. 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.,
  9. 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.,
  10. Stefano Messelodi; Carla Maria Modena; Gianni Cattoni,
    vol. 28,
    n. 13,
    , pp. 1719 -