A connection between image processing and artificial neural networks layers through a geometric model of visual perception

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• dc.contributor.author Batard, Thomas
• dc.contributor.author Maldonado, Eduard Ramon
• dc.contributor.author Steidl, Gabriele
• dc.contributor.author Bertalmío, Marcelo
• dc.date.accessioned 2019-12-18T14:49:39Z
• dc.date.available 2019-12-18T14:49:39Z
• dc.date.issued 2019
• dc.description Comunicació presentada a: the 7th International Conference on Scale Space and Variational Methods in Computer Vision, celebrada del 30 de juny al 4 de juliol de 2019 a Hofgeismar, Alemanya.
• dc.description.abstract In this paper, we establish a connection between image processing, visual perception, and deep learning by introducing a mathematical model inspired by visual perception from which neural network layers and image processing models for color correction can be derived. Our model is inspired by the geometry of visual perception and couples a geometric model for the organization of some neurons in the visual cortex with a geometric model of color perception. More precisely, the model is a combination of a Wilson-Cowan equation describing the activity of neurons responding to edges and textures in the area V1 of the visual cortex and a Retinex model of color vision. For some particular activation functions, this yields a color correction model which processes simultaneously edges/textures, encoded into a Riemannian metric, and the color contrast, encoded into a nonlocal covariant derivative. Then, we show that the proposed model can be assimilated to a residual layer provided that the activation function is nonlinear and to a convolutional layer for a linear activation function. Finally, we show the accuracy of the model for deep learning by testing it on the MNIST dataset for digit classiffication.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Batard T, Maldonado ER, Steidl G, Bertalmío M. A connection between image processing and artificial neural networks layers through a geometric model of visual perception. In: Lellmann J, Burger M, Modersitzki J, editors. Scale Space and Variational Methods in Computer Vision. The 7th International Conference on Scale Space and Variational Methods in Computer Vision; 2019 Jun 30-Jul 4; Hofgeismar, Germany. [Cham]: Springer; 2019. p. 459-71. (LNCS; no. 11.603). DOI: 10.1007/978-3-030-22368-7_36
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-030-22368-7_36
• dc.identifier.issn 0302-9743
• dc.identifier.uri http://hdl.handle.net/10230/43207
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Lellmann J, Burger M, Modersitzki J, editors. Scale Space and Variational Methods in Computer Vision. The 7th International Conference on Scale Space and Variational Methods in Computer Vision; 2019 Jun 30-Jul 4; Hofgeismar, Germany. [Cham]: Springer; 2019. p. 459-71. (LNCS; no. 11.603).
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-22368-7_36
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Differential geometryen
• dc.subject.keyword Variational model
• dc.subject.keyword Image processingen
• dc.subject.keyword Visionen
• dc.subject.keyword Neural networken
• dc.title A connection between image processing and artificial neural networks layers through a geometric model of visual perception
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion