19 May 1992 Maximum-likelihood morphological granulometric classifiers
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Abstract
The moments of local morphological granulometric pattern spectra are employed to classify texture images, the novelty being the use of maximum-likelihood techniques do design the classifier. Classification is adapted to the presence of noise and minimal feature sets are obtained. Using a database of ten textures, it is seen that a small number of granulometric moments from among the mean, variance, and skewness (resulting from a small set of structuring primitives) is sufficient to achieve very high accuracy for independent data in the absence of noise, and to maintain high accuracy in the face of some commonplace noise types so long as good noise estimates are available.
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John T. Newell, John T. Newell, Edward R. Dougherty, Edward R. Dougherty, } "Maximum-likelihood morphological granulometric classifiers", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58344; https://doi.org/10.1117/12.58344
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