Texture Characterization via Joint Statistics of Wavelet Coefficient Magnitudes

E P Simoncelli and J Portilla

Published in Proc 5th IEEE Int'l Conf on Image Proc, vol.I pp. 62--66, Oct 1998.
© IEEE Signal Processing Society
DOI: 10.1109/ICIP.1998.723417

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    • We present a parametric statistical characterization of texture images in the context of an overcomplete complex wavelet frame. The characterization consists of the local autocorrelation of the coefficients in each subband, the local autocorrelation of the cofficent magnitudes, and the cross-correlation of coefficient magnitudes at all orientations and adjacent spatial scales. We develop an efficient algorithm for sampling from an implicit probability density conforming to these statistics, and demonstrate its effectiveness in synthesizing artificial and natural texture images.
    Related:
  • Int'l Journal of Computer Vision, 2000: AbstractA parametric texture model based on joint statistics of complex wavelet coefficients
    by J Portilla and E P Simoncelli
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