Introduction

Abstract

Computer vision has been an active area of research in computer science for over fifty years. Much progress has been made on many fronts by researchers proposing a variety of algorithms motivated by many other fields of science, including mathematics, physics, physiology, and biology. These algorithms are often focused on specific tasks such as image segmentation, classification, or image registration. Some common approaches to many algorithms is that they employ a variety of linearizing approximations to simplify the computations. Chaos theory is a field of research that provides insight into how dynamical systems can exhibit radically different behaviors from seemingly similar initial conditions. It is based on the ideas of non-linear dynamics and provides a set of tools for understanding these complex behaviors. In this text we embrace the non-linear foundations of chaos theory and employ them to solve a broad variety of computer vision applications.

Keywords: Chaos theory, information, phase space, biological vision systems.

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