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Texture classification using a spatial-point process model

Texture classification using a spatial-point process model

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A Bayesian statistical classifier for the segmentation of texture is presented, which models the quantised image data as a set of independent spatial Poisson processes. Two data sets are examined, namely Gaussian white noise textures, and textures contained in a sidescan sonar image of the seabed. The Poisson model is demonstrated to be applicable in both these cases, and a maximum likelihood discriminant function is developed. Finally, results are presented for the classification of both data sets.

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