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.
|Number of pages||6|
|Journal||IEE Proceedings: Vision, Image and Signal Processing|
|Publication status||Published - Feb 1995|