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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="http://torrentfreak.com/the-copyright-lobby-absolutely-loves-child-pornography-110709/?"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1503.03741">
    <title>[1503.03741] 2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA</title>
    <dc:date>2015-04-10T11:48:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1503.03741</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %.
]]></description>
<dc:subject>face-recognition filtering representation machine-learning data-cleaning nudge-targets algorithms</dc:subject>
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<dc:identifier>https://pinboard.in/u:Vaguery/b:9e03a1828f76/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1208.3718">
    <title>[1208.3718] Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal</title>
    <dc:date>2012-08-25T12:12:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1208.3718</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm."]]></description>
<dc:subject>image-processing filtering optimization inference nudge-targets algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:96cbe847e722/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1110.0585">
    <title>[1110.0585] Discriminately Decreasing Discriminability with Learned Image Filters</title>
    <dc:date>2011-12-16T13:00:30+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.0585</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discriminability of one classification task (a "distractor" task), while simultaneously preserving information relevant to another (the task-of-interest): For example, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Another example is inter-dataset generalization: when training on a dataset with a particular covariance structure among multiple attributes, it may be useful to suppress one attribute while preserving another so that a trained classifier does not learn spurious correlations between attributes. In this paper we present an algorithm that finds optimal filters to give high discriminability to one task while simultaneously giving low discriminability to a distractor task. We present results showing the effectiveness of the proposed technique on both simulated data and natural face images."]]></description>
<dc:subject>machine-learning data-preparation filtering algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:049d916a133a/</dc:identifier>
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<item rdf:about="http://torrentfreak.com/the-copyright-lobby-absolutely-loves-child-pornography-110709/?">
    <title>The Copyright Lobby Absolutely Loves Child Pornography | TorrentFreak</title>
    <dc:date>2011-07-10T11:08:37+00:00</dc:date>
    <link>http://torrentfreak.com/the-copyright-lobby-absolutely-loves-child-pornography-110709/?</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The conclusion is as unpleasant as it is inevitable. The copyright industry lobby is actively trying to hide egregious crimes against children, obviously not because they care about the children, but because the resulting censorship mechanism can be a benefit to their business if they manage to broaden the censorship in the next stage. All this in defense of their lucrative monopoly that starves the public of culture."]]></description>
<dc:subject>copyright intellectual-property corporatism public-policy pornography freedom-of-expression filtering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:33a8f2354ad6/</dc:identifier>
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<item rdf:about="http://www.librarything.com/thingology/2008/07/google-goes-after-library-of-congress.php">
    <title>Thingology (LibraryThing's ideas blog): Google goes after the Library of Congress for &quot;mature content&quot;</title>
    <dc:date>2008-08-11T18:30:04+00:00</dc:date>
    <link>http://www.librarything.com/thingology/2008/07/google-goes-after-library-of-congress.php</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["I have accordingly been consulting with Casey on how to remove all the butt-shots from the Yale University MARC records."
]]></description>
<dc:subject>Google censorship LibraryThing filtering natural-language-processing FAIL</dc:subject>
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    <title>See no evil? Doctorow at The Grauniad</title>
    <dc:date>2007-06-06T21:04:12+00:00</dc:date>
    <link>http://commentisfree.guardian.co.uk/cory_doctorow/2007/06/see_no_evil.html</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>via:armchair_anarchist openness censorship filtering freedom government politics policy social-planning community</dc:subject>
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