Texture classification by gray-scale morphological granulometries

Yidong Chen; Edward R. Dougherty

doi:10.1117/12.131505

1 November 1992 Texture classification by gray-scale morphological granulometries

Yidong Chen, Edward R. Dougherty

Proceedings Volume 1818, Visual Communications and Image Processing '92; (1992) https://doi.org/10.1117/12.131505
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

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.

Citation Download Citation

Yidong Chen and Edward R. Dougherty "Texture classification by gray-scale morphological granulometries", Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); https://doi.org/10.1117/12.131505

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