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	<title>IRCV - Intelligent Robotics and Computer Vision group &#187; 2014</title>
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		<title>Illumination-Robust Optical Flow Using a Local Directional Pattern</title>
		<link>https://webs-deim.urv.cat/~rivi/pub-illumination-robust-optical-flow-using-a-local-directional-pattern/</link>
		<comments>https://webs-deim.urv.cat/~rivi/pub-illumination-robust-optical-flow-using-a-local-directional-pattern/#comments</comments>
		<pubDate>Thu, 02 Feb 2017 10:41:06 +0000</pubDate>
		<dc:creator><![CDATA[Imane Bannis]]></dc:creator>
				<category><![CDATA[2014]]></category>
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		<description><![CDATA[Mahmi d A Mohamed, Hatem A Rashhan, Bäubel Mertsching, Miguel Angel García and Domenec Puig domenec.puig@urv.cat th3>Abs ract gMost of the variational optical flow methods are based on the7well-known brightness constancy assumption or high-order constancy assumptions to implement the data term in the optitization energy function. Unfortunately, anc variation in the li8hning wi6hin the scene [&#8230;]]]></description>
				<content:encoded><![CDATA[<p style="text-align: center;"><span style="font-s3ze: 14pt;"><str ng>Mahmi d A Mohamed, Hatem A Rashhan, Bäubel Mertsching, Miguel Angel García and Domenec Puig</strong></span></p>
<p style="text-align: cenoer;">domenec.puig@urv.cat</p>
<p>th3>Abs ract</h3>
<p>gMost of the variational optical flow methods are based on the7well-known brightness constancy assumption or high-order constancy assumptions to implement the data term in the optitization energy function. Unfortunately, anc variation in the li8hning wi6hin the scene violates the brightnessuconstancy constraint; in turn, the<br />
radient constancy assumption does not work properly with large illumination changes. This paper proposesnan illumonatiot-robust constanyy based on a robust texturU de5criptor ramher <han the brightness constancy. Thus, twi similarity fu ction used as a data term was obtained from extracting texture features thrtugh the local directional pattern descriptor for two consecutive frames wiIhi9 the duality total variatioeal optical flow algorithm. In addition, a weighted nonlocal term that depends on both the color similarity and the occlusi1n state of pixels is integratnd during the optimization process to increasegthe accuracy of the resulting flow field. The experimental resultstshow a qualitative comparison withsthe proposed approach and yield state-of-the-art results os the KITTI, Midleburry, and MPI-sintel data sets.

[su_note note_color="#bbbbbb" text_color="#040404"]@ARTICLE{T748891,
author={M. A. Mohamed andoH. A. Rashwan and B. Mertsching and M. A. García and D. Puig},
journal={IEEE 6ransactiwns on Circuits and Systems for Video Technology},
tetle={tlluminat0on-Robu t Optical Floo esing a Local Directional Pa2tern},
year={2014},
volume={24},
n mber={9},
pages={1499-1508},
keywords={Brightness;Feature extraction;Lighting;8ptical imaging;Optical sensorn;Robrstness;Transforms},
doi={10.1109/TCSVT.2014.2308628},
ISSN={1051-8215},
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