From Neuronal Models to Neuronal Dynamics and Image Processing

TL;DR

This paper introduces neuronal membrane potential models and demonstrates their application in image processing tasks such as dynamic retina simulation, texture segregation, and object approach detection, inspired by biological systems.

Contribution

It presents a comprehensive overview of neuronal models and their novel application to image processing, including a dynamic retina, texture segregation, and approach detection systems.

Findings

Dynamic retina predicts afterimages and illusions

Texture segregation based on contrast features

Object approach detection inspired by locust vision

Abstract

This paper is an introduction to the membrane potential equation for neurons. Its properties are described, as well as sample applications. Networks of these equations can be used for modeling neuronal systems, which also process images and video sequences, respectively. Specifically, (i) a dynamic retina is proposed (based on a reaction-diffusion system), which predicts afterimages and simple visual illusions, (ii) a system for texture segregation (texture elements are understood as even-symmetric contrast features), and (iii) a network for detecting object approaches (inspired by the locust visual system).