Home > Publications database > Channel smoothing: Efficient robust smoothing of low-level signal features > print

001     47026
005     20190625111831.0
024 7 _ |2 pmid
|a pmid:16468618
024 7 _ |2 DOI
|a 10.1109/TPAMI.2006.29
024 7 _ |2 WOS
|a WOS:000233824500004
024 7 _ |a altmetric:21814083
|2 altmetric
037 _ _ |a PreJuSER-47026
041 _ _ |a eng
082 _ _ |a 620
084 _ _ |2 WoS
|a Computer Science, Artificial Intelligence
084 _ _ |2 WoS
|a Engineering, Electrical & Electronic
100 1 _ |a Felsberg, R. E.
|b 0
|0 P:(DE-HGF)0
245 _ _ |a Channel smoothing: Efficient robust smoothing of low-level signal features
260 _ _ |a New York, NY
|b IEEE
|c 2006
300 _ _ |a 209 - 222
336 7 _ |a Journal Article
|0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
440 _ 0 |a IEEE Transactions on Pattern Analysis and Machine Intelligence
|x 0162-8828
|0 14841
|y 2
|v 28
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a 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.
536 _ _ |a Terrestrische Umwelt
|c P24
|2 G:(DE-HGF)
|0 G:(DE-Juel1)FUEK407
|x 0
588 _ _ |a Dataset connected to Web of Science, Pubmed
650 _ 2 |2 MeSH
|a Algorithms
650 _ 2 |2 MeSH
|a Artificial Intelligence
650 _ 2 |2 MeSH
|a Data Compression: methods
650 _ 2 |2 MeSH
|a Image Enhancement: methods
650 _ 2 |2 MeSH
|a Image Interpretation, Computer-Assisted: methods
650 _ 2 |2 MeSH
|a Numerical Analysis, Computer-Assisted
650 _ 2 |2 MeSH
|a Pattern Recognition, Automated: methods
650 _ 2 |2 MeSH
|a Signal Processing, Computer-Assisted
650 _ 7 |a J
|2 WoSType
653 2 0 |2 Author
|a robust smoothing
653 2 0 |2 Author
|a channel representation
653 2 0 |2 Author
|a diffusion filtering
653 2 0 |2 Author
|a bilateral filtering
653 2 0 |2 Author
|a mean-shift
653 2 0 |2 Author
|a B-spline
653 2 0 |2 Author
|a orientation smoothing
700 1 _ |a Forssén, V. T.
|b 1
|0 P:(DE-HGF)0
700 1 _ |a Scharr, H.
|b 2
|u FZJ
|0 P:(DE-Juel1)129394
773 _ _ |a 10.1109/TPAMI.2006.29
|g Vol. 28, p. 209 - 222
|p 209 - 222
|q 28<209 - 222
|0 PERI:(DE-600)2027336-8
|t IEEE transactions on pattern analysis and machine intelligence
|v 28
|y 2006
|x 0162-8828
856 7 _ |u http://dx.doi.org/10.1109/TPAMI.2006.29
909 C O |o oai:juser.fz-juelich.de:47026
|p VDB
913 1 _ |k P24
|v Terrestrische Umwelt
|l Terrestrische Umwelt
|b Erde und Umwelt
|0 G:(DE-Juel1)FUEK407
|x 0
914 1 _ |y 2006
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |k ICG-III
|l Phytosphäre
|d 31.12.2006
|g ICG
|0 I:(DE-Juel1)VDB49
|x 0
970 _ _ |a VDB:(DE-Juel1)74133
980 _ _ |a VDB
980 _ _ |a ConvertedRecord
980 _ _ |a journal
980 _ _ |a I:(DE-Juel1)IBG-2-20101118
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IBG-2-20101118
981 _ _ |a I:(DE-Juel1)ICG-3-20090406

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21