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Entropy-Based Texture Analysis in the Spatial Frequency Domain
February 1984 (vol. 6 no. 2)
pp. 237-243
M. E. Jernigan, Department of Systems Design, University of Waterloo, Waterloo, Ont., Canada N2L 3G1.
F. D'Astous, Department of Systems Design, University of Waterloo, Waterloo, Ont., Canada N2L 3G1.
An approach which uses regional entropy measures in the spatial frequency domain for texture discrimination is presented. The measures provide texture discriminating information independent of that contained in the usual summed energy within based frequency domain features. Performance of the entropy features as measured by a between-to-within-class scatter criterion is comparable to that of traditional frequency domain features and gray level co-occurrence contrast features. A method of frequency scaling is introduced to enable the comparison of texture samples of different subimage size. The resulting regional entropy measures are subimage size-invariant subject to certain constraints which arise from properties of the discrete Fourier transform.
Citation:
M. E. Jernigan, F. D'Astous, "Entropy-Based Texture Analysis in the Spatial Frequency Domain," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 2, pp. 237-243, Feb. 1984, doi:10.1109/TPAMI.1984.4767507
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