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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 Learnintg & 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)


  • 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. M. Lecca,
    Object Recognition in Color Images by the Self Configuring System MEMORI,
    vol. 3,
    n. 3,
    , pp. 176 -
  2. 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)
  3. 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)
  4. 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.
  5. 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)
  6. M. Lecca,
    MEMORI - Version 1.0,
    This report contains a detailed description of the latest version of MEMORI, a system for the automatic detection and recognition of the objects of a database within digital color images. A comparison with the previous version is also presented,
  7. M. Lecca,
    A new method for the automatic estimation of the heuristic rule parameters for MEMORI 1.0,
  8. C. Andreatta; M. Lecca; S. Messelodi,
    RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
    , (RIAO 2004. Coupling approaches, coupling media and coupling languages for information retrieval,
  9. C. Andreatta; M. Lecca; S. Messelodi,
    Memory based object recognition in images,
    MEMORI is a system for the detection and recognition of objects, stored in a database, within digital images taken as an input. It aims to provide a content-based indexing scheme which exploits the semantic information contained in the images. Object detection is achieved by segmenting the input image using color and textural information, and grouping the obtained regions by analyzing their adjacency relationships and their visual similarity to the objects in the database. The database is the memory of the system and consists of objects depicted by a set of different views, each of them described by a feature vector encoding color, texture and shape information. The same description is computed for the region groups candidated to represent an object and is compared, by an object classifier, with the database objects. The final result is a number of image regions each associated to one or more object classes with a confidence score,
  10. Michela Lecca,
    Object Retrieval in Digital Images Using Subgraph Isomorphism,
    This work presents a method for object recognition in digital images based on Graph Theory. We aim at establishing if a given object is present in an image. To do this, we describe the object and the image by a labeled topological graph. The search of the object in the image is done testing the existence of a subgraph isomorphism between the topological graph associated to the object and the topological graph associated to the image. Our method to detect labeled subgraph isomorphisms is based on an analysis of the breadth first search trees and on the construction of an isomorphism by an extension procedure. The method has been applied to the object retrieval in digital images; each object and image region represented by the vertices of the topological graph is described by a feature vector; the similarity between two regions is measured calculating the L1-distance between the corresponding feature vectors; the L1 - norm is used also to define a cost for each isomorphism; by a thresholding strategy based on the distance analysis only the isomorphisms of interest can be selected,