Multivariate morphological granulometric texture classification using Walsh and wavelet features

16 June 1995 Multivariate morphological granulometric texture classification using Walsh and wavelet features

Sinan Batman, Edward R. Dougherty, Mark C. Rzadca, Joseph O. Chapa

Proceedings Volume 2488, Visual Information Processing IV; (1995) https://doi.org/10.1117/12.212011
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

As introduced by Matheron, granulometries depend on a single sizing parameter for each structuring element forming the filter. The size distributions resulting from these granulometries have been used successfully to classify texture by using as features the moments of the normalized size distribution. The present paper extends the concept of granulometry in such a way that each structuring element has its own sizing parameter and the resulting size distribution is multivariate. Classification is accomplished by taking either the Walsh or wavelet transform of the multivariate size distribution, obtaining a reduced feature set by applying the Karhunen-Loeve transform to decorrelate the Walsh or wavelet features, and classifying the textures via a Gaussian maximum-likelihood classifier.

Citation Download Citation

Sinan Batman, Edward R. Dougherty, Mark C. Rzadca, and Joseph O. Chapa "Multivariate morphological granulometric texture classification using Walsh and wavelet features", Proc. SPIE 2488, Visual Information Processing IV, (16 June 1995); https://doi.org/10.1117/12.212011

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