Texture Characterization via Joint Statistics
of Wavelet Coefficient Magnitudes
Published in:
Proc. 5th Int'l Conference on Image Processing
Chicago, IL. October 4-7, 1998.
doi: 10.1109/ICIP.1998.723417
© IEEE Signal Processing Society.
Presentation slides (pdf, 800k).
Full-length journal article:
Int'l Journal of Computer Vision, 2000
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.
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