Abstract

The theory of Autopoiesis describes what the living systems are and not what they do. Instead of investigating the behavior of systems exhibiting autonomy and the concrete implementation of this autonomy (i.e. the system structure), the study addresses the reason why such behavior is exhibited (i.e. the abstract system organization). This article explores the use of autopoietic concepts in the field of Image Processing. Two different approaches are presented. The first approach assumes that the organization of an image is represented only by its grayvalue distribution. In order to identify autopoietic organization inside an image's pixel distribution, the steady state Xor-operation is identified as the only valid approach for an autopoietic processing of images. The effect of its application on images is explored and discussed. The second approach makes use of a second space, the A-space, as the autopoietic-processing domain. This allows for the formulation of adaptable recognition tasks. Based on this second approach, the concept of autopoiesis as a tool for the analysis of textures is explored.. More specifically, a computational model of autopoiesis is applied for texture identification through the use of an autopoietic-agent.