https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Lee_Gao_2015aLee Gao 2015a - Revision history2026-04-22T00:42:49ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Lee_Gao_2015a&diff=194246&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 185330380 to Lee Gao 2015a2021-01-28T20:39:20Z<p>Scipediacontent moved page <a href="/public/Draft_Content_185330380" class="mw-redirect" title="Draft Content 185330380">Draft Content 185330380</a> to <a href="/public/Lee_Gao_2015a" title="Lee Gao 2015a">Lee Gao 2015a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Lee_Gao_2015a&diff=194245&oldid=prevScipediacontent: Created page with " == Abstract == Vehicle analysis is an import task in many intelligent applications, such as automatic toll collection and driver assistance systems. Among these applications..."2021-01-28T20:39:15Z<p>Created page with " == Abstract == Vehicle analysis is an import task in many intelligent applications, such as automatic toll collection and driver assistance systems. Among these applications..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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Vehicle analysis is an import task in many intelligent applications, such as automatic toll collection and driver assistance systems. Among these applications, moving car detection and model recognition are a challenging task due to the close appearance between car models. In this paper, we propose a framework to detect moving cars and its model based on deep learning. We first detect the moving car using frame difference; the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is used to identify a car based on deep learning with three layers of restricted Boltzmann machines (RBMs). Experiment results show that our proposed framework achieves favorable recognition accuracy.<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [http://dx.doi.org/10.14257/astl.2015.90.13 http://dx.doi.org/10.14257/astl.2015.90.13]<br />
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* [http://pdfs.semanticscholar.org/66fb/b53bc1996db6e11358550530fec78b736008.pdf http://pdfs.semanticscholar.org/66fb/b53bc1996db6e11358550530fec78b736008.pdf]<br />
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* [http://onlinepresent.org/proceedings/vol90_2015/13.pdf http://onlinepresent.org/proceedings/vol90_2015/13.pdf],<br />
: [https://academic.microsoft.com/#/detail/2329814265 https://academic.microsoft.com/#/detail/2329814265]</div>Scipediacontent