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In this paper we focus on distances between textures, and develop metrics on image spaces in contexts of image transformations. Given a metric on the range space, we can generate the initial topology for the domain space. For this topology we can obtain a corresponding metric using well-known metrization constructions, also providing granularity of the metrics. Examples are drawn from the Spatial Gray Level Dependency (SGLD) transformation and the application domain is texture recognition in medical imaging.

Unraveling the Thrill of Metric Image Spaces