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Computational Color Constancy

Our research is concerned with estimating a color correction between images observing the same scene but acquired under different illuminants, viewpoints, and/or by different devices.

Many computer vision methods rely on color features, which are determined by the intrinsic properties of objects and surfaces, as well as the color of the light sources and spectral sensitivity of the capturing device. Color variations induced by these latter sources should, however, often be discarded in solving the computer vision task at hand. Computational color constancy - i.e. the ability to implement invariance to light source and device - is then a desired feature of computer vision systems for indexing and retrieval, recognition, tracking, and many more. 

Reference publications:

M. Lecca, A. Rizzi, R.P. Serapioni: GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex, IEEE Transactions on Image Processing, 26(6):2767-2780, 2017

M. Lecca, A. Rizzi, R.P. Serapioni: GREAT: a gradient-based color-sampling scheme for Retinex, Journal of the Optical Society of America A - Optics Image Science and Vision, 34(4):513-522, 2017

G. Simone, E. Cordone, R.P. Serapioni, M. Lecca: On Edge-Aware Path-based Color Spatial Sampling for Retinex: from Termite Retinex to Light Energy-driven Termite Retinex, SPIE Journal of Electronic Imaging, 26(3), 2017

M. Lecca, A. Rizzi, G. Gianini: Energy-driven path search for Termite Retinex, Journal of the Optical Society of America A - Optics Image Science and Vision, 33(1):31-39, 2016

M. Lecca, A. Rizzi: Tuning the locality of filtering with a spatially weighted implementation of random spray Retinex, Journal of the Optical Society of America A - Optics Image Science and Vision, 10(32):1876-1887, 2015

M. Lecca: A Full Linear 3x3 Color Correction between Images. Journal of Real-Time Image Processing, vol. 10, n. 2, 2015, pp. 219-237

M. Lecca: On the von Kries Model: Estimation, Dependence on Light and Device, and Applications. Chapter in Book, Advances in Low-Level Color Image Processing, Lecture Notes in Computational Vision and Biomechanics, Vol. 11, M. E. Celebi, B. Smolka Eds., Springer, 2014, pp. 95-135, ISBN 978-94-007-7583-1

M.E. Celebi, M. Lecca, B. Smolka Eds., Color Image and Video Enhancement, Springer, 2015

S. Mutlu, S. Rota Bulo', O. Lanz: Exploiting Color Constancy for Robust Tracking Under non-Uniform Illumination. International Conference on Image Analysis and Recognition - ICIAR, Algavre, Portugal, 22-24 October 2014

M. Lecca, Color improves texture retrieval, X Conferenza del Colore, Genoa, Italy, 2013

S. Mutlu, T. Hu, O. Lanz: Learning the Scene Illumination for Color-Based People Tracking in Dynamic Environment, International Conference on Image Analysis and Processing - ICIAP, Naples, Italy, September 2013

M. Lecca, Methods for Estimating the von Kries Transform: A Review and A Comparison, IX Conferenza del Colore, Firenze, Italy, 2013

M. Lecca, S. Messelodi: Linking the von Kries Model to Wien's Law for the estimation of an Illuminant Invariant ImagePattern Recognition Letters, Vol. 32, No. 15, pp. 2086-2096, 2011

Research topics: