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Encoding Visual Information Using Anisotropic Transformations
February 2001 (vol. 23 no. 2)
pp. 207-211

Abstract—The evolution of information in images undergoing fine-to-coarse anisotropic transformations is analyzed by using an approach based on the theory of irreversible transformations. In particular, we show that, when an anisotropic diffusion model is used, local variation of entropy production over space and scale provides the basis for a general method to extract relevant image features.

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Index Terms:
Scale space, anisotropic diffusion, entropy production, feature encoding.
Citation:
Giuseppe Boccignone, Mario Ferraro, Terry Caelli, "Encoding Visual Information Using Anisotropic Transformations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 207-211, Feb. 2001, doi:10.1109/34.908970
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