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000001797 0247_ $$2pmid$$apmid:20634558
000001797 0247_ $$2DOI$$a10.1109/TPAMI.2009.162
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000001797 041__ $$aeng
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000001797 084__ $$2WoS$$aComputer Science, Artificial Intelligence
000001797 084__ $$2WoS$$aEngineering, Electrical & Electronic
000001797 1001_ $$0P:(DE-Juel1)VDB61695$$aSchuchert, T.$$b0$$uFZJ
000001797 245__ $$aRange Flow in Varying Illumination: Algorithms and Comparisons
000001797 260__ $$aNew York, NY$$bIEEE$$c2010
000001797 300__ $$a1646 - 1658
000001797 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000001797 440_0 $$014841$$aIEEE Transactions on Pattern Analysis and Machine Intelligence$$v32$$x0162-8828$$y9
000001797 500__ $$aRecord converted from VDB: 12.11.2012
000001797 520__ $$aWe extend estimation of range flow to handle brightness changes in image data caused by inhomogeneous illumination. Standard range flow computes 3D velocity fields using both range and intensity image sequences. Toward this end, range flow estimation combines a depth change model with a brightness constancy model. However, local brightness is generally not preserved when object surfaces rotate relative to the camera or the light sources, or when surfaces move in inhomogeneous illumination. We describe and investigate different approaches to handle such brightness changes. A straightforward approach is to prefilter the intensity data such that brightness changes are suppressed, for instance, by a highpass or a homomorphic filter. Such prefiltering may, though, reduce the signal-to-noise ratio. An alternative novel approach is to replace the brightness constancy model by 1) a gradient constancy model, or 2) by a combination of gradient and brightness constancy constraints used earlier successfully for optical flow, or 3) by a physics-based brightness change model. In performance tests, the standard version and the novel versions of range flow estimation are investigated using prefiltered or nonprefiltered synthetic data with available ground truth. Furthermore, the influences of additive Gaussian noise and simulated shot noise are investigated. Finally, we compare all range flow estimators on real data.
000001797 536__ $$0G:(DE-Juel1)FUEK407$$2G:(DE-HGF)$$aTerrestrische Umwelt$$cP24$$x0
000001797 588__ $$aDataset connected to Web of Science, Pubmed
000001797 650_2 $$2MeSH$$aAlgorithms
000001797 650_2 $$2MeSH$$aArtifacts
000001797 650_2 $$2MeSH$$aArtificial Intelligence
000001797 650_2 $$2MeSH$$aImage Enhancement: methods
000001797 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000001797 650_2 $$2MeSH$$aImaging, Three-Dimensional: methods
000001797 650_2 $$2MeSH$$aLighting: methods
000001797 650_2 $$2MeSH$$aPattern Recognition, Automated: methods
000001797 650_7 $$2WoSType$$aJ
000001797 65320 $$2Author$$aRange flow
000001797 65320 $$2Author$$aillumination changes
000001797 65320 $$2Author$$abrightness constancy constraint
000001797 65320 $$2Author$$aprefiltering
000001797 65320 $$2Author$$ahomomorphic filter
000001797 65320 $$2Author$$agradient constancy
000001797 65320 $$2Author$$astructure tensor
000001797 65320 $$2Author$$a3D motion estimation
000001797 7001_ $$0P:(DE-Juel1)VDB87595$$aAach, T.$$b1$$uFZJ
000001797 7001_ $$0P:(DE-Juel1)129394$$aScharr, H.$$b2$$uFZJ
000001797 773__ $$0PERI:(DE-600)2027336-8$$a10.1109/TPAMI.2009.162$$gVol. 32, p. 1646 - 1658$$p1646 - 1658$$q32<1646 - 1658$$tIEEE transactions on pattern analysis and machine intelligence$$v32$$x0162-8828$$y2010
000001797 8567_ $$uhttp://dx.doi.org/10.1109/TPAMI.2009.162
000001797 909CO $$ooai:juser.fz-juelich.de:1797$$pVDB
000001797 9131_ $$0G:(DE-Juel1)FUEK407$$bErde und Umwelt$$kP24$$lTerrestrische Umwelt$$vTerrestrische Umwelt$$x0
000001797 9132_ $$0G:(DE-HGF)POF3-582$$1G:(DE-HGF)POF3-580$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lKey Technologies for the Bioeconomy$$vPlant Science$$x0
000001797 9141_ $$y2010
000001797 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000001797 9201_ $$0I:(DE-Juel1)ICG-3-20090406$$d31.10.2010$$gICG$$kICG-3$$lPhytosphäre$$x1
000001797 9201_ $$0I:(DE-82)080010_20140620$$gJARA$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x2
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