Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling
This work addresses the problem of understanding and simulating visual perception biases for researchers in computational neuroscience and image processing, though it is incremental as it builds on existing models.
The authors tackled the modeling of orientation-dependent contrast perception phenomena, such as grating induction and a modified Poggendorff illusion, by developing a cortical-inspired Wilson-Cowan-type model that integrates local directional information from the primary visual cortex. The model successfully reproduces these visual biases and empirically identifies threshold parameters that differentiate between inpainting and perception-type reconstructions, describing long-range connectivity in V1.
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 the 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 thus defined in the space of positions and orientation and it is capable to describe assimilation and contrast visual bias at the same time. We report several numerical tests showing the ability of the model to reproduce, 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 and describing long-range connectivity between different hypercolumns in V1.