A Parametric Texture Model based on
Joint Statistics of Complex Wavelet Coefficients
Published in:
International Journal of Computer Vision
40(1): 49-71, October 2000.
© Kluwer Academic Publishers.
Previous 5-page conference publication:
ICIP-98
We present a universal statistical model for texture images in the
context of an overcomplete complex wavelet transform. The model is
parameterized by a set of statistics computed on pairs of coefficients
corresponding to basis functions at adjacent spatial locations,
orientations, and scales. We develop an efficient algorithm for
synthesizing random images subject to these constraints, by
iteratively projecting onto the set of images satisfying each
constraint, and we use this to test the perceptual validity of the
model. In particular, we demonstrate the necessity of subgroups of
the parameter set by showing examples of texture synthesis that fail
when those parameters are removed from the set. We also demonstrate
the power of our model by successfully synthesizing examples drawn
from a diverse collection of artificial and natural textures.
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