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@ARTICLE{Felsberg:47026,
      author       = {Felsberg, R. E. and Forssén, V. T. and Scharr, H.},
      title        = {{C}hannel smoothing: {E}fficient robust smoothing of
                      low-level signal features},
      journal      = {IEEE transactions on pattern analysis and machine
                      intelligence},
      volume       = {28},
      issn         = {0162-8828},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {PreJuSER-47026},
      pages        = {209 - 222},
      year         = {2006},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {In this paper, we present a new and efficient method to
                      implement robust smoothing of low-level signal features:
                      B-spline channel smoothing. This method consists of three
                      steps: encoding of the signal features into channels,
                      averaging of the channels, and decoding of the channels. We
                      show that linear smoothing of channels is equivalent to
                      robust smoothing of the signal features if we make use of
                      quadratic B-splines to generate the channels. The linear
                      decoding from B-spline channels allows the derivation of a
                      robust error norm, which is very similar to Tukey's biweight
                      error norm. We compare channel smoothing with three other
                      robust smoothing techniques: nonlinear diffusion, bilateral
                      filtering, and mean-shift filtering, both theoretically and
                      on a 2D orientation-data smoothing task. Channel smoothing
                      is found to be superior in four respects: It has a lower
                      computational complexity, it is easy to implement, it
                      chooses the global minimum error instead of the nearest
                      local minimum, and it can also be used on nonlinear spaces,
                      such as orientation space.},
      keywords     = {Algorithms / Artificial Intelligence / Data Compression:
                      methods / Image Enhancement: methods / Image Interpretation,
                      Computer-Assisted: methods / Numerical Analysis,
                      Computer-Assisted / Pattern Recognition, Automated: methods
                      / Signal Processing, Computer-Assisted / J (WoSType)},
      cin          = {ICG-III},
      ddc          = {620},
      cid          = {I:(DE-Juel1)VDB49},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Computer Science, Artificial Intelligence / Engineering,
                      Electrical $\&$ Electronic},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:16468618},
      UT           = {WOS:000233824500004},
      doi          = {10.1109/TPAMI.2006.29},
      url          = {https://juser.fz-juelich.de/record/47026},
}