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000047026 0247_ $$2DOI$$a10.1109/TPAMI.2006.29
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000047026 041__ $$aeng
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000047026 084__ $$2WoS$$aComputer Science, Artificial Intelligence
000047026 084__ $$2WoS$$aEngineering, Electrical & Electronic
000047026 1001_ $$0P:(DE-HGF)0$$aFelsberg, R. E.$$b0
000047026 245__ $$aChannel smoothing: Efficient robust smoothing of low-level signal features
000047026 260__ $$aNew York, NY$$bIEEE$$c2006
000047026 300__ $$a209 - 222
000047026 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000047026 440_0 $$014841$$aIEEE Transactions on Pattern Analysis and Machine Intelligence$$v28$$x0162-8828$$y2
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000047026 520__ $$aIn 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.
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000047026 650_2 $$2MeSH$$aAlgorithms
000047026 650_2 $$2MeSH$$aArtificial Intelligence
000047026 650_2 $$2MeSH$$aData Compression: methods
000047026 650_2 $$2MeSH$$aImage Enhancement: methods
000047026 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000047026 650_2 $$2MeSH$$aNumerical Analysis, Computer-Assisted
000047026 650_2 $$2MeSH$$aPattern Recognition, Automated: methods
000047026 650_2 $$2MeSH$$aSignal Processing, Computer-Assisted
000047026 650_7 $$2WoSType$$aJ
000047026 65320 $$2Author$$arobust smoothing
000047026 65320 $$2Author$$achannel representation
000047026 65320 $$2Author$$adiffusion filtering
000047026 65320 $$2Author$$abilateral filtering
000047026 65320 $$2Author$$amean-shift
000047026 65320 $$2Author$$aB-spline
000047026 65320 $$2Author$$aorientation smoothing
000047026 7001_ $$0P:(DE-HGF)0$$aForssén, V. T.$$b1
000047026 7001_ $$0P:(DE-Juel1)129394$$aScharr, H.$$b2$$uFZJ
000047026 773__ $$0PERI:(DE-600)2027336-8$$a10.1109/TPAMI.2006.29$$gVol. 28, p. 209 - 222$$p209 - 222$$q28<209 - 222$$tIEEE transactions on pattern analysis and machine intelligence$$v28$$x0162-8828$$y2006
000047026 8567_ $$uhttp://dx.doi.org/10.1109/TPAMI.2006.29
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000047026 9141_ $$y2006
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