Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Mathematical Imaging and Vision Year : 2020

Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling

Abstract

We consider the evolution model proposed in [9, 6] to describe illusory contrast perception phenomena induced by surrounding orientations. Firstly, we highlight its analogies and differences with widely used Wilson-Cowan equations [48], mainly in terms of efficient representation properties. Then, in order to explicitly encode local directional information, we exploit the model of the primary visual cortex V1 proposed in [20] and largely used over the last years for several image processing problems [24, 38, 28]. The resulting model is capable to describe assimilation and contrast visual bias at the same time, the main novelty being its explicit dependence on local image orientation. We report several numerical tests showing the ability of the model to explain, in particular, orientation-dependent phenomena such as grating induction and a modified version of the Poggendorff illusion. For this latter example, we empirically show the existence of a set of threshold parameters differentiating from inpainting to perception-type reconstructions, describing long-range connectivity between different hypercolumns in the primary visual cortex.
Fichier principal
Vignette du fichier
InvitedJMIV_WCeq.pdf (1.57 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02316989 , version 1 (15-10-2019)

Identifiers

Cite

Marcelo Bertalmio, Luca Calatroni, Valentina Franceschi, Benedetta Franceschiello, Dario Prandi. Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling. Journal of Mathematical Imaging and Vision, 2020, ⟨10.1007/s10851-020-00960-x⟩. ⟨hal-02316989⟩
189 View
153 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More