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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Sudipta Sinha</author_name><author_url>https://www.microsoft.com/en-us/research/people/sudipsin/</author_url><title>Graph Cut Algorithms in Vision, Graphics and Machine Learning - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="nwMUT4aISf"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/248342/"&gt;Graph Cut Algorithms in Vision, Graphics and Machine Learning&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/248342/embed/#?secret=nwMUT4aISf" width="600" height="338" title="&#x201C;Graph Cut Algorithms in Vision, Graphics and Machine Learning&#x201D; &#x2014; Microsoft Research" data-secret="nwMUT4aISf" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>This integrative paper studies graph-cut and network flow algorithms on graphs and compares its applications towards solving diverse problems in Computer Vision, Computer Graphics and Machine Learning. The following three papers form the core of this comparative study. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision by Boykov et.al.[1] Graph Cut Textures: [&hellip;]</description></oembed>
