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.

Key words: autopoiesis, steady state, image processing, auto-projective operators, texture analysis, texture retrieval, systems, autopoietic-agents.

Upshot: We acknowledge that our model can be implemented with different reinforcement learning algorithms. Subsystem formation has been successfully demonstrated on the basal level, and in order to show full subsystem formation in the communication system at least both intentional utterances and acceptance/rejection need to be implemented. The comments about intrinsic vs extrinsic rewards made clear that this distinction is not helpful in the context of the constructivist paradigm but rather needs to be replaced by a critical reflection on whether one has truly created autopoietic agents or just an engineering system.