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Autopoiesis and Image Processing: Detection of Structure and Organization in Images

机译：自我生成和图像处理：图像中结构和组织的检测

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The theory of Autopoiesis describes what the living systems are and not what they do. Instead of investigating the behavior of systems exhibiting autnomy 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 asthe 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.
机译：自生理论描述的是生命系统，而不是生命系统。该研究没有研究表现出自主性的系统的行为以及这种自主性的具体实现（即系统结构），而是研究了表现出这种行为的原因（即抽象系统组织）。本文探讨了自体概念在图像处理领域的使用。提出了两种不同的方法。第一种方法假定图像的组织仅由其灰度值分布表示。为了识别图像像素分布内的自体组织，将稳态Xor操作标识为图像自体处理的唯一有效方法。探索和讨论了其应用对图像的影响。第二种方法利用第二个空间A空间作为自体处理域。这允许制定适应性识别任务。基于第二种方法，探索了将自生作用作为纹理分析工具的概念。

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