Proximity Law Modelling for Quantifying the Visual Perception by Marked Point Process

The human visual system receives sensory inputs from the environment and converts them into the perception of real objects such as desks, buildings, and cars. This phenomenon is known as visual perception. It is an effortless process which makes sense of the visual information. Recently, the ultimate goal for machine vision researches is to imitate the human visual perception and to understand the intricate data processing done by the human brain. In this paper, we propose a methodology inspired by Gestalt theory of perception to quantify the human visual perception. Our goal is to add a quantitative aspect for the visual perception in order to be easily integrated into the image processing tasks. Tests on synthetic images show good performance on images with Gaussian noise proving its efficiency to detect perceptual groups.