% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Schuchert:1797,
      author       = {Schuchert, T. and Aach, T. and Scharr, H.},
      title        = {{R}ange {F}low in {V}arying {I}llumination: {A}lgorithms
                      and {C}omparisons},
      journal      = {IEEE transactions on pattern analysis and machine
                      intelligence},
      volume       = {32},
      issn         = {0162-8828},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {PreJuSER-1797},
      pages        = {1646 - 1658},
      year         = {2010},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {We 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.},
      keywords     = {Algorithms / Artifacts / Artificial Intelligence / Image
                      Enhancement: methods / Image Interpretation,
                      Computer-Assisted: methods / Imaging, Three-Dimensional:
                      methods / Lighting: methods / Pattern Recognition,
                      Automated: methods / J (WoSType)},
      cin          = {ICG-3 / JARA-BRAIN},
      ddc          = {620},
      cid          = {I:(DE-Juel1)ICG-3-20090406 / $I:(DE-82)080010_20140620$},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Computer Science, Artificial Intelligence / Engineering,
                      Electrical $\&$ Electronic},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:20634558},
      UT           = {WOS:000279969000008},
      doi          = {10.1109/TPAMI.2009.162},
      url          = {https://juser.fz-juelich.de/record/1797},
}