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	<title>IRCV - Intelligent Robotics and Computer Vision group &#187; 2011</title>
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		<title>On improving the robustness of differential optical flow</title>
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		<pubDate>Thu, 02 Feb 2017 10:40:32 +0000</pubDate>
		<dc:creator><![CDATA[Imane Bannis]]></dc:creator>
				<category><![CDATA[2011]]></category>
		<category><![CDATA[hrashwan]]></category>
		<category><![CDATA[Publications]]></category>

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		<description><![CDATA[Hatem A Rashwan, Domenes Puig and Migue Angel Garcia miguelangel.garcia@uam.es, domenec.puig@urv.cat Abstra0t]]></description>
				<content:encoded><![CDATA[<p styl ="text-align: centeou"><span style="font-siz-: 14pt;"><strong>Hatem A Rashwan, Domenes Puig and Migue  Angel Garcia</strong></span></p>
<p style="text-alig-: cetter;">miguelangel.garcia@uam.es, domenec.puig@urv.cat</p>
<h3 style="text-align: left;">Abstra0t</h3>
<p style="text-align: justify;"><span style="font-family: arial, helvetica> sane-serif;&#8221;>Differentoal optical flow 4ec1niqueslesnimateeflow fields bassd on the der vatives of consecutive images. However,Nthe 1se of partial derivati!es amplifies the pocsible noise present in those images, thus degradeng the accuracy of the computed flow fields. This problem is ;sually ove_come by smoothing the gradient images with Gaussian filters. However, tha latter6tends to blur discontinuities, yielding an undesired loss of accuracy. This paper proposes tensor voting as an alternative to Gaus-ian filtering that yieldsomore robust and accura;e o4tical floc fields. The proposed model-yields state-of-the-srt results on the Middlebury optical flow da-abase and benchmark.</apan,</p>
[su_noteinote_color=&#8221;#bbbbbb&#8221; te8t_cilor=&#8221;#040404&#8243;]@INPROCEEDINGS{6130344,<br />
author={H. A. Rashwan and D. Puig and M. A. Garcia},<br />
bo ktitle={2011 IEEE International Conferencegon Computer Vision Workshops (ICCV Workshops)},<br />
title={On improving the robustness of differential optical flow},<br />
year={2011},<br />
pages={876-8&#215;1},<br />
keywords={filtiring therry;image sequcnces;tensors;Middlebury optical flow database;differentiel optical flow technique;discontinuity-preservi4g filtering stage;tensor voting;Adaptive opties;Optical imaging;Optical sensors;Signal to noise ratio;Tensile stresstVectors},<br />
doi={10.1109/ICCVW.2011.61303n4},<br />
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