Document Zbl 1203.94037

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However, when these data are not stationary, estimating the parameters, which are required for unsupervised processing, poses a problem. Moreover, taking into account correlated non-Gaussian noise is difficult without model approximations. The aim of this paper is to propose a simultaneous solution to both of these problems using triplet Markov chains (TMC) and copulas. The interest of the proposed models and related processing is validated by different experiments some of which are related to semi-supervised and unsupervised image segmentation.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A5830246">Cited in <strong>6</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A94A12" title="MSC2020">94A12</a> </td> <td class="space"> Signal theory (characterization, reconstruction, filtering, etc.) </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Ahidden+Markov+chains">hidden Markov chains</a>; <a href="/?q=ut%3Atriplet+Markov+chains">triplet Markov chains</a>; <a href="/?q=ut%3Aunsupervised+signal+segmentation">unsupervised signal segmentation</a>; <a href="/?q=ut%3Anon-stationary+data+segmentation">non-stationary data segmentation</a>; <a href="/?q=ut%3Aimage+segmentation">image segmentation</a>; <a href="/?q=ut%3Atexture+classification">texture classification</a>; <a href="/?q=ut%3Acopulas">copulas</a>; <a href="/?q=ut%3Anon-Gaussian+noise">non-Gaussian noise</a>; <a href="/?q=ut%3Acorrelated+noise">correlated noise</a>; <a href="/?q=ut%3Astochastic+EM">stochastic EM</a>; <a href="/?q=ut%3Aiterative+conditional+estimation">iterative conditional estimation</a></div>  <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;">  </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function">  <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1203.94037" data-ciurl="/ci/05830246" data-biburl="/bibtex/05830246.bib" data-amsurl="/amsrefs/05830246.bib" data-xmlurl="/xml/05830246.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/05830246.pdf" title="Zbl 1203.94037 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1016/j.sigpro.2010.05.033" aria-label="DOI for “Unsupervised segmentation of randomly switching data hidden with non-Gaussian correlated noise”" title="10.1016/j.sigpro.2010.05.033">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Ait-El-Fquih, B.; Desbouvries, F.: Kalman filtering in triplet Markov chains, IEEE transactions on signal processing 54, No. 8, 2957-2963 (2006) · <a href="/1373.93335" class="nowrap">Zbl 1373.93335</a></td> </tr><tr> <td>[2]</td> <td class="space">Benboudjema, D.; Pieczynski, W.: Unsupervised statistical segmentation of non stationary images using triplet Markov fields, IEEE transactions on pattern analysis and machine intelligence 29, No. 8, 1367-1378 (2007)</td> </tr><tr> <td>[3]</td> <td class="space">Bishop, Y. 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