Texture classification by gray-scale morphological granulometries

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Abstract
Binary morphological granulometric size distributions were conceived by Matheron as a way of describing image granularity (or texture). Since each normalized size distribution is a probability density, feature vectors of granulometric moments result. Recent application has focused on taking local size distributions around individual pixels so that the latter can be classified by surrounding texture. The present paper investigates the extension of the local- classification technique to gray-scale textures. It does so by using forty-two granulometric features, half generated by opening granulometries and a dual half generated by closing granulometries. After training and classification of both dependent and independent data, feature extraction (compression) is accomplished by means of the Karhunen-Loeve transform. The effect of randomly placed Gaussian noise is investigated.

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