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Batard T, Maldonado ERamon, Steidl G, Bertalmío M.  2019.  A connection between image processing and artificial neural networks layers through a geometric model of visual perception. Scale Space and Variational Methods in Computer Vision (SSVM2019). geometricModelPerception.pdf (13.46 MB)
Batard T, Bertalmío M.  2014.  On Covariant Derivatives and Their Applications to Image Regularization. SIAM Journal on Imaging Sciences (SIIMS). SIIMS_Denoising.pdf (4 MB)
Batard T, Bertalmío M.  2016.  A Class of Nonlocal Variational Problems on a Vector Bundle for Color Image Local Contrast Reduction/Enhancement. *Accepted* in Geometry, Imaging and Computing. preprint2.pdf (11.12 MB)
Bertalmío M.  2016.  Connections between Retinex, neural models and variational methods. IS&T Electronic Imaging Conference. BertalmioRetinex50_EI2016.pdf (152.17 KB)
Bertalmío M., Levine S..  2013.  Color matching for stereoscopic cinema. Proceedings of Mirage 2013, 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications. Berlin. paper.pdf (7.52 MB)
Bertalmío M, Batard T, Kim J.  2016.  Correcting for Induction Phenomena on Displays of Differrent Size. Vision Sciences Society Annual Meeting.
Bertalmío M, Calatroni L, Franceschi V, Franceschiello B, Prandi D.  2019.  A cortical-inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations. Scale Space and Variational Methods in Computer Vision (SSVM2019). ContrastPerception.pdf (669.04 KB)