Pixel Classification By Morphologically Derived Texture Features

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Abstract
Local granulometric size distributions are generated by performing a granulometry on an image and keeping local pixel counts in a neighborhood of each pixel at the completion of each successive opening. Normalization of the resulting size distributions yields a probability density at each pixel. These densities contain texture information local to each pixel. Pixels can be classified according to the moments of the densities. Further refinement can be accomplished by employing several structuring-element sequences in order to generate a number of granulometries, each revealing different texture qualities. Classification is accomplished by comparing observed moments to those representing a database of textures. The collection of database moments are actually random variables dependent on random texture processes, and the method employed in the present paper involves the comparison of observed moments to the means of database-texture moments.
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