Piecewise smooth signal denoising via principal curve projections
buir.contributor.author | Arıkan, Orhan | |
buir.contributor.orcid | Arıkan, Orhan|0000-0002-3698-8888 | |
dc.citation.epage | 431 | en_US |
dc.citation.spage | 426 | en_US |
dc.contributor.author | Özertem, Umut | en_US |
dc.contributor.author | Erdoğmuş, D. | en_US |
dc.contributor.author | Arıkan, Orhan | en_US |
dc.coverage.spatial | Cancun, Mexico | |
dc.date.accessioned | 2016-02-08T11:36:27Z | |
dc.date.available | 2016-02-08T11:36:27Z | |
dc.date.issued | 2008-10 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description | Date of Conference: 16-19 Oct. 2008 | |
dc.description | Conference name: 2008 IEEE Workshop on Machine Learning for Signal Processing | |
dc.description.abstract | One common problem in signal denoising is that if the signal has a blocky, in other words a piecewise-smooth structure, the denoised signal may suffer from oversmoothed discontinuities or exhibit artifacts very similar to Gibbs phenomenon. In the literature, total variation methods and some modifications on the signal reconstructions based on wavelet coefficients are proposed to overcome these problems. We take a novel approach by introducing principal curve projections as an artifact-free signal denoising filter alternative. The proposed approach leads to a nonparametric denoising algorithm that does not lead to Gibbs effect or so-called staircase type unnatural artifacts in the denoised signal. ©2008 IEEE. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T11:36:27Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2008 | en |
dc.identifier.doi | 10.1109/MLSP.2008.4685518 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/26810 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | |
dc.relation.isversionof | http://dx.doi.org/10.1109/MLSP.2008.4685518 | en_US |
dc.source.title | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 | en_US |
dc.subject | Learning systems | en_US |
dc.subject | Robot learning | en_US |
dc.subject | Technical presentations | en_US |
dc.subject | Common problems | en_US |
dc.subject | Denoised signals | en_US |
dc.subject | Denoising algorithms | en_US |
dc.subject | Gibbs phenomenons | en_US |
dc.subject | Nonparametric | en_US |
dc.subject | Piece wises | en_US |
dc.subject | Piecewise smoothes | en_US |
dc.subject | Principal curves | en_US |
dc.subject | Signal denoising | en_US |
dc.subject | Total variations | en_US |
dc.subject | Wavelet coefficients | en_US |
dc.subject | Signal processing | en_US |
dc.title | Piecewise smooth signal denoising via principal curve projections | en_US |
dc.type | Conference Paper | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Piecewise smooth signal denoising via principal curve projections.pdf
- Size:
- 3.54 MB
- Format:
- Adobe Portable Document Format
- Description:
- Full printable version