Journal ArticleDOI
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
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TLDR
A universal statistical model for texture images in the context of an overcomplete complex wavelet transform is presented, demonstrating the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set.Abstract:
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.read more
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Proceedings ArticleDOI
How Well Do Computational Features Perceptually Rank Textures? A Comparative Evaluation
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References
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
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Journal ArticleDOI
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Orthonormal bases of compactly supported wavelets
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Journal ArticleDOI
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