Reduced complexity rotation invariant texture classification using a blind deconvolution approach.

Patrizio Campisi; Stefania Colonnese; Gianpiero Panci; Gaetano Scarano

doi:10.1109/tpami.2006.24

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Reduced complexity rotation invariant texture classification using a blind deconvolution approach.

Patrizio Campisi·Stefania Colonnese·Gianpiero Panci·Gaetano Scarano

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Abstract

In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.

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Algorithms Artificial Intelligence Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Pattern Recognition, Automated Rotation Statistics as Topic Evaluation Study Journal Article

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Patrizio Campisi, Stefania Colonnese, Gianpiero Panci, Gaetano Scarano. 2006. Reduced complexity rotation invariant texture classification using a blind deconvolution approach.. https://doi.org/10.1109/tpami.2006.24

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