@Bibtex-file{Graphics/Seemann.bib,
  title =        "Torsten Seemann's BibTe{X} Bibliography",
  author =       "Torsten Seemann",
  email =        "torsten@csse.monash.edu.au",
  address =      "School of Computer Science and Software
                 Engineering\\Monash University\\Clayton VIC
                 3800\\Australia\\Tel: +61 3 9905 9010",
  supported =    "no",
  abstract =     "This is bibliography for the Ph.D thesis {"}Digital
                 Image Processing using Local Segmentation{"}:\\ A
                 unifying philosophy for carrying out low level image
                 processing called `local segmentation'' is presented.
                 Local segmentation provides a way to examine and
                 understand existing algorithms, as well as a paradigm
                 for creating new ones. Local segmentation may be
                 applied to range of important image processing tasks.
                 Using a traditional segmentation technique in intensity
                 thresholding and a simple model selection criterion,
                 the new FUELS denoising algorithm is shown to be highly
                 competitive with state-of-the-art algorithms on a range
                 of images. In an effort to improve the local
                 segmentation, the minimum message length information
                 theoretic criterion for model selection (MML) is used
                 to select between models having different structure and
                 complexity. This leads to further improvements in
                 denoising performance. Both FUELS and the MML variants
                 thereof require no special user supplied parameters,
                 but instead learn from the image itself. It is believed
                 that image processing in general could benefit greatly
                 from the application of the local segmentation
                 methodology.",
  keywords =     "digital image processing, local segmentation",
}