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Statistical Techniques for Unsupervised Segmentation and Classification

Statistical Techniques for Unsupervised Segmentation and Classification. TCD Interests Simon Wilson. Work so far. MRF-based greyscale image segmentation of textured images; IEEE Trans. Sig Proc, 2002, vol. 50, no. 2, pp. 357-365; Both labels and textures are modelled by MRFs (double MRF);

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Statistical Techniques for Unsupervised Segmentation and Classification

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  1. Statistical Techniques for Unsupervised Segmentation and Classification TCD Interests Simon Wilson

  2. Work so far • MRF-based greyscale image segmentation of textured images; • IEEE Trans. Sig Proc, 2002, vol. 50, no. 2, pp. 357-365; • Both labels and textures are modelled by MRFs (double MRF); • Assumes no. of classes known, parameters of label and texture fields unknown; • Approach implemented with MCMC and simulated annealing to find MAP; • Applied to segmentation of satellite images; • Later work with INRIA-Ariana extended to case of unknown no. of classes – didn’t work so well – INRIA RR

  3. Research Questions • These methods could be used in blind restoration/reconstruction with some appropriate modifications • Statistical methods of text recognition implemented through MCMC

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