We consider a differential model describing neuro-physiologi-cal contrast perception phenomena induced by surrounding orientations. The mathematical formulation relies on a cortical-inspired modelling [11] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [14, 15, 21]. Our model connects to Wilson-Cowan-type equations [26] and it is analogous to the one used in [3, 4, 16] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confirm the validity of the model, we report some numerical tests showing its ability to explain orientation-dependent phenomena (such as grating induction) and geometric-optical illusions [18, 24] classically explained only by filtering-based techniques [7, 20].

A Cortical-Inspired Model for Orientation-Dependent Contrast Perception: A Link with Wilson-Cowan Equations

Franceschi V.;
2019

Abstract

We consider a differential model describing neuro-physiologi-cal contrast perception phenomena induced by surrounding orientations. The mathematical formulation relies on a cortical-inspired modelling [11] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [14, 15, 21]. Our model connects to Wilson-Cowan-type equations [26] and it is analogous to the one used in [3, 4, 16] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confirm the validity of the model, we report some numerical tests showing its ability to explain orientation-dependent phenomena (such as grating induction) and geometric-optical illusions [18, 24] classically explained only by filtering-based techniques [7, 20].
2019
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
978-3-030-22367-0
978-3-030-22368-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3363854
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