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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)

News:

  • 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
Publications
  1. Michela Lecca,
    Methods for Estimating the von Kries Transform: A Review and A Comparison,
    Proc. of IX Color Conference,
    2013
    , (IX Color Conference,
    Firenze, Italia,
    Sept. 19-20, 2013)
  2. Michela Lecca; Mauro Dalla Mura,
    Intensity and Affine Invariant Statistic-based Image Matching,
    Computational Modeling of Objects Presented in Images,
    CRC Press
    Taylor & Francis Group
    ,
    2012
    , pp. 85-
    90
    , (CompImage 2012 - Computational Modeling of Objects Presented in Images: Fundamentals, Methods, and Applications,
    Rome - Italy,
    09/05/2012 - 09/07/2012)
  3. Michela Lecca,
    Histogram-based Estimation of Illuminant Changes: von Kries Model versus Diagonal-Offset Model,
    Technical Report

    Abstract:

    A same scene captured under different lights produces two different color images. Such a color variation is usually approximated by the von Kries model, that relates the color responses of the images by a diagonal linear map. In order to describe also image noise, diffuse lights possibly disturbing the scene or lens scattering, an offset term is often added.
    In this work, we compare the diagonal model and the diagonal-offset model in terms of accuracy on color correction and illuminant invariant image retrieval. Our experiments show just slight differences between the performances of the two models, that provide satisfactory results.

    ,
    2012
  4. Michela Lecca,
    Geometric Distortions of Color Channels for Linear Color Correction Between Images,
    Technical Report

    Abstract - A change of color between two pictures of a same scene captured by different devices and/or under different illuminants, is usually approximated by a full 3x3 linear map between the RGB responses of the images. By assuming this model, we develop a novel efficient color correction algorithm, with linear complexity in the number of image pixels. The main idea underlying our approach is to describe the images to be corrected by nine channels: the red, green, blue color channels provided by the device, plus six other channels encoding both geometric and color information. These additional channels are built up from the color ones by distorting them with 2D functions related to the pixel positions. The color correction is achieved by a linear transform which maps the mean values of the nine channels of first image on the mean values of the nine channels of the second images.

    ,
    2012
  5. M. Lecca; S. Messelodi,
    Linking the von Kries Model to Wien's Law for the estimation of an Illuminant Invariant Image,
    in «PATTERN RECOGNITION LETTERS»,
    vol. 32,
    n. 15,
    2011
    , pp. 2086 -
    2086
  6. M. Lecca; S. Messelodi,
    Von Kries Model Under Planckian Illuminants,
    Proceedings of ICIAP 2011,
    Springer,
    vol.1,
    2011
    , pp. 296-
    305
    , (International Conference on Image Analysis and Processing,
    Ravenna, ITALY,
    from 09/14/2011 to 09/16/2011)
  7. M. Lecca; S. Messelodi,
    Von Kries Model Under Planckian Illuminants: An Empirical Analysis,
    This is a technical report, that investigate the relation between two models for describing the variation of the colors due to a photmetric change.

    Abstract:
    Planckian illuminants and the von Kries diagonal model are commonly assumed by many computer vision algorithms for modeling the color variations between two images of a same scene captured under two different illuminants.
    In this work we provide a method to estimate a von Kries transform approximating a Planckian illuminant change and we show that the Planckian assumption constraints the von Kries coefficients to belong to a ruled surface depending on physical cues of the lights. Moreover, we provide an approximated parametric representation of such a surface, making evident the dependence of the von Kries transform on the light color temperature and on the intensity.

    ,
    2011
  8. Michela Lecca,
    Statistics-based Estimate of Affine Transforms between Images Without Correspondences,
    Technical Report,
    2011
  9. 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.

    ,
    2010
  10. Michela Lecca; Stefano Messelodi,
    Computing von Kries Illuminant Changes by Piecewise Inversion of Cumulative Color Histograms,
    in «ELCVIA. ELECTRONIC LETTERS ON COMPUTER VISION AND IMAGE ANALYSIS»,
    2009
    , pp. 1 -
    17

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