A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Proceedings of the 43rd annual Design Automation Conference
Independent component analysis-based defect detection in patterned liquid crystal display surfaces
Image and Vision Computing
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Journal of Mathematical Imaging and Vision
Non-negative sparse modeling of textures
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.