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Torsten Seemann's BibTeX Bibliography

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Number of references:327Last update:March 11, 2003
Number of online publications:0Supported:no
Most recent reference:2003

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Information on the Bibliography

Torsten Seemann <torsten @ csse . monash . edu . au> (email mangled to prevent spamming)
School of Computer Science and Software Engineering
Monash University
Clayton VIC 3800
Tel: +61 3 9905 9010
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.
digital image processing, local segmentation

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Bibliographic Statistics

article(166), inproceedings(103), book(35), techreport(12), phdthesis(4), incollection(2), mastersthesis(2), misc(2), inbook(1)
title(327), year(325), author(320), pages(268), volume(185), journal(166), keywords(154), publisher(121), number(110), month(109), booktitle(104), note(51), url(49), address(23), editor(22), institution(13), organization(6), series(6), abstract(5), edition(5), school(5), addresss(1), chapter(1), editors(1), optnote(1), urlnote(1)
Distribution of publication dates:
Distribution of publication dates

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