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| 001 | 824589 | ||
| 005 | 20210129225059.0 | ||
| 037 | _ | _ | |a FZJ-2016-07156 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kleefeld, Andreas |0 P:(DE-Juel1)169421 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a IAS Symposium 2016 |c Jülich |d 2016-12-05 - 2016-12-06 |w Germany |
| 245 | _ | _ | |a Adaptive Filters for Color Images: Median Filtering and its Extensions |
| 260 | _ | _ | |c 2016 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1481094579_4424 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a In this talk, the construction of structure-preserving denoising filters for color images is explained. This is based on a recently proposed transformation from the RGB color space to the space of symmetric $2\times2$ matrices that has already been used to transfer morphological operations such as dilation and erosion from matrix-valued data to color images (see [1]).The applicability of this framework is shown for the construction ofcolor-valued median filters. Additionally, spatial adaptivity is introducedinto this approach by morphological amoebas that offer excellent capabilities for structure-preserving filtering. Furthermore, color-valued amoeba M-smoothers as a generalization of the median-basedconcepts are defined. The experiments confirm that all these methods work wellwith color images. They demonstrate the potential of the new approach todefine color processing tools based on matrix field techniques (refer to [2]).[1] Burgeth, B., Kleefeld A. (2014) An approach to color-morphology based on Einstein addition and Loewner order, Pattern Recognition Letters, 47, 29-39.[2] Kleefeld, A. et al. (2015) Adaptive Filters for Color Images: Median Filtering and its Extensions, Lecture Notes in Computer Science, Springer, Berlin, 9016, 149-158. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 909 | C | O | |o oai:juser.fz-juelich.de:824589 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)169421 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2016 |
| 915 | _ | _ | |a No Authors Fulltext |0 StatID:(DE-HGF)0550 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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