Maximum-likelihood morphological granulometric classifiers

John T. Newell; Edward R. Dougherty

doi:10.1117/12.58344

19 May 1992 Maximum-likelihood morphological granulometric classifiers

John T. Newell, Edward R. Dougherty

Proceedings Volume 1657, Image Processing Algorithms and Techniques III; (1992) https://doi.org/10.1117/12.58344
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

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.

Citation Download Citation

John T. Newell and Edward R. Dougherty "Maximum-likelihood morphological granulometric classifiers", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); https://doi.org/10.1117/12.58344

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