You are here

Michela Lecca

Permanent researcher
  • Phone: 0461314536
  • FBK Povo
Short bio

Michela received her Master Degree in Mathematics from the University of Trento, Italy. She is currently researcher at the Research Unit Technologies of Vision of Fondazione Bruno Kessler of Trento, Italy. Her research interests include automatic object recognition, image retrieval, semantic image labeling, color constancy, color correction, and low-level image processing. From 2014, Michela collaborates with the research Unit IRIS (FBK-CMM) on hardware-oriented computer vision topics.

Michela took part to the program committee of many editions of Applied Computing Machine Symposia, and carries on a reviewing activity for several international conferences. She is a member of the International Association for Pattern Recognition -  Associazione Italiana per la ricerca in Computer Vision, Pattern recognition e machine Learning IAPR - CVPL (ex GIRPR) and of the Gruppo Italiano del Colore - Associazione Italiana Colore (GdC-AIC).

FBK Learning & Development Courses:

Michela participated to the following FBK training activities:

  • Infographics & data visualization (17/04/2018)
  • Comunicare FBK (10/11/2017)
  • Comunicare la Scienza / Media Writing (14/09/2017)


  • January 24, 2019: Michela illustated the cover of the book Theoretical Physics for Biological Systems, now available on market.
  • January 07, 2019: a web app of Forensor is avaiable on
  • November-December 2018: Michela collaborates with University of Verona (IT), Dept. of Medicine to develop a software for detecting organoids and computing their volumes over time.  A first verison of this tool has been presented at SIAM - IS, in Bologna (IT), June 2018,  and at the 32nd Annual North America Cystic Fibrosis Conference, in USA, October 2018.
  • August 2018: Michela is a Guest Editor of the Special Issue on J. of Imaging Image Enhancement, Modeling and Visualization. The call for paper is published on the journal website, deadline on August 12, 2018 (EXTENDED!)
  • February 2018: Michela was a co-organizer of the Workshop "Mathematics for Computer Vision" (MCV 2018), hosted by FBK - ICT on February 15-16, 2018.
  • September 2017: Michela, along with her colleagues at IRIS, participated at the Notte dei Ricercatori and presented some outcomes of the EU Project FORENSOR (Visione Intelligente a Basso Consumo / Ecco la telecamera anti-crimine!).
  • March 2017: Michela presented a Tutorial on Retinex theory at the 6th Computational Color Imaging Workshop (Milano, Italy.
  • Since December 2016, Michela is a co-author of the Column Communcations and Comments of the Journal "Cultura e Scienza del Colore - Color Culture and Science of the Gruppo del Colore - Associazione Italiana Colore.
  • 2015-2016: Michela led a Research in Pairs with the University of Trento, Department of Mathematics, and the University of Milano, Department of Computer Science. "Research in Pairs" is a program of FBK-CIRM, that promotes the collaboration among different research centers and/or university on topics mainly related to Mathematics. The research in pairs involving Michela focuses on the development of variational models for estimating the human color sensation. The main findings of this research have been published on JOSA A  and on IEEE TIP
Research interests
object recognition semantic image labeling color constancy color correction hardware oriented image processing
  1. 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.,
  2. Michela Lecca; Stefano Messelodi,
    18th British Machine Vision Conference,
    , pp. 112-
    , (18th British Machine Vision Conference,
    Coventry, United Kingdom,
    10/09/2007 - 13/09/2007)
  3. P. Lecca; Michela Lecca,
    Molecular Mechanism of Glutamate-Triggered Brain Glucose Metabolism: A Parametric Model from FDG PET-Scans,
    Advanced in Brain Vision and Artificial Intelligence,
    , pp. 350-
    , (International Symposium on Brain, Vision and Artificial Intelligence - BVAI,
    Napoli, Italy,
    09/10/2007 - 12/10/2007)
  4. M. Lecca; S. Messelodi; C. Andreatta,
    An Object Recognition System for Automatic Image Annotation and Browsing of Object Catalogs,
    New York,
    , pp. 154-
    , (15th International Conference on Multimedia - ACMMM,
    Augsburg, Germany,
    from 09/24/2007 to 09/29/20007)
  5. M. Lecca,
    A Self Configuring System for Object Recognition in Color Images,
    vol. 12,
    , pp. 245 -
  6. M. Lecca,
    Object Recognition in Color Images by the Self Configuring System MEMORI,
    vol. 3,
    n. 3,
    , pp. 176 -
  7. M. Lecca,
    A Self Configuring System for Object Recognition in Color Images,
    World Academy of Science, Engineering and Technology,
    , pp. 35-
    , (12th International Conference on Computer Science,
    Vienna, Austria,
    03/29/2006 - 03/31/2006)
  8. M. Lecca; S. Messelodi,
    Recognition and Reconstruction of Partially Occluded Objects,
    XVI International Conference on Computer Science,
    World Enformatika Society,
    , pp. 233-
    , (XVI International Conference on Computer Science,
    Venice, Italy,
    24/11/2006 - 26/11/2006)
  9. M. Lecca,
    Recognition and reconstruction of partially occluded objects,
    Abstract - A new automatic system for the recognition and reconstruction of rescaled and/or rotated partially occluded objects is presented. The objects to recognize are described by many 2D views and each view is occluded by half-planes with different slopes. The remaining parts (linear cuts) and the whole object views are then stored in a database. To establish if a region R of an input image represents an object possibly occluded, the system generates a set of
    linear cuts of R and compare them with the elements in the database. Each linear cut of R is associated to the most similar database linear cut. R is recognized as an instance of the object O if the most of the linear cuts of R are associated to a linear cut of views of O. In the case of recognition, the system selects the region cut and the correspondent view cut C (O) whose log-polar transforms match as
    best as possible and uses them to reconstruct the whole shape of R. The scale factor and orientation in image plane of R with respect C (O) are determined.
  10. C. Andreatta; M. Lecca; S. Messelodi,
    Memory-based Object Recognition in digital Images,
    AKA Akademische Verlagsgesellschaft,
    , pp. 33-
    , (19th International Fall Workshop- Vision Modeling and Visualization,
    Erlangen, Germany,
    11/16/2005 - 11/18/2005)