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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Stochastic-Variational Model for Soft Mumford-Shah Segmentation</title><author><name sortKey="Shen, Jianhong Jackie" sort="Shen, Jianhong Jackie" uniqKey="Shen J" first="Jianhong Jackie" last="Shen">Jianhong Jackie Shen</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">23165059</idno><idno type="pmc">2324060</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2324060</idno><idno type="RBID">PMC:2324060</idno><idno type="doi">10.1155/IJBI/2006/92329</idno><date when="2006">2006</date><idno type="wicri:Area/Pmc/Corpus">000284</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A Stochastic-Variational Model for Soft Mumford-Shah Segmentation</title><author><name sortKey="Shen, Jianhong Jackie" sort="Shen, Jianhong Jackie" uniqKey="Shen J" first="Jianhong Jackie" last="Shen">Jianhong Jackie Shen</name><affiliation><nlm:aff>NONE</nlm:aff></affiliation><affiliation><nlm:aff>NONE</nlm:aff></affiliation></author></analytic><series><title level="j">International Journal of Biomedical Imaging</title><idno type="ISSN">1687-4188</idno><idno type="eISSN">1687-4196</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In contemporary image and vision analysis, stochastic approaches
demonstrate great flexibility in representing and modeling complex
phenomena, while variational-PDE methods gain enormous
computational advantages over Monte Carlo or other stochastic
algorithms. In combination, the two can lead to much more powerful
novel models and efficient algorithms. In the current work, we
propose a stochastic-variational model for <italic>soft</italic> (or
fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike
the classical <italic>hard</italic> Mumford-Shah segmentation, the new
model allows each pixel to belong to each image pattern with some
probability. Soft segmentation could lead to hard segmentation,
and hence is more general. The modeling procedure, mathematical
analysis on the existence of optimal solutions, and computational
implementation of the new model are explored in detail, and
numerical examples of both synthetic and natural images are
presented.</p></div></front></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Int J Biomed Imaging</journal-id><journal-id journal-id-type="publisher-id">IJBI</journal-id><journal-title>International Journal of Biomedical Imaging</journal-title><issn pub-type="ppub">1687-4188</issn><issn pub-type="epub">1687-4196</issn><publisher><publisher-name>Hindawi Publishing Corporation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">23165059</article-id><article-id pub-id-type="pmc">2324060</article-id><article-id pub-id-type="doi">10.1155/IJBI/2006/92329</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A Stochastic-Variational Model for Soft Mumford-Shah Segmentation</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Shen</surname><given-names>Jianhong (Jackie)</given-names></name><xref ref-type="aff" rid="I1"><sup>1, 2</sup></xref><xref ref-type="aff" rid="I2"></xref></contrib></contrib-group><aff id="I1"><sup>1</sup> School of Mathematics, Institute of Technology, University of Minnesota, Minneapolis, MN 55455, USA</aff><aff id="I2"><sup>2</sup> Lotus Hill Institute for Computer Vision and Information Science, E'Zhou, Wuhan 436000, China</aff><author-notes><corresp id="cor1">*: Jianhong (Jackie) Shen
</corresp></author-notes><pub-date pub-type="ppub"><year>2006</year></pub-date><pub-date pub-type="epub"><day>12</day><month>4</month><year>2006</year></pub-date><volume>2006</volume><elocation-id>92329</elocation-id><history><date date-type="received"><day>20</day><month>9</month><year>2005</year></date><date date-type="rev-recd"><day>13</day><month>2</month><year>2006</year></date><date date-type="accepted"><day>17</day><month>2</month><year>2006</year></date></history><permissions><copyright-statement>Copyright © IJBI J. Shen </copyright-statement><copyright-year>2006</copyright-year><license license-type="open-access"><p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license></permissions><abstract><p>In contemporary image and vision analysis, stochastic approaches
demonstrate great flexibility in representing and modeling complex
phenomena, while variational-PDE methods gain enormous
computational advantages over Monte Carlo or other stochastic
algorithms. In combination, the two can lead to much more powerful
novel models and efficient algorithms. In the current work, we
propose a stochastic-variational model for <italic>soft</italic> (or
fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike
the classical <italic>hard</italic> Mumford-Shah segmentation, the new
model allows each pixel to belong to each image pattern with some
probability. Soft segmentation could lead to hard segmentation,
and hence is more general. The modeling procedure, mathematical
analysis on the existence of optimal solutions, and computational
implementation of the new model are explored in detail, and
numerical examples of both synthetic and natural images are
presented.</p></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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