Unraveling the Thrill of Metric Image Spaces
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.
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