liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Advanced Filter Design
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
1999 (English)In: Proceedings of the 11th Scandinavian Conference on Image Analysis: Greenland, SCIA , 1999, 185-193 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a general approach for obtaining optimal filters as well as filter sequences. A filter is termed optimal when it minimizes a chosen distance measure with respect to an ideal filter. The method allows specification of the metric via simultaneous weighting functions in multiple domains, e.g. the spatio-temporal space and the Fourier space. Metric classes suitable for optimization of localized filters for multidimensional signal processing are suggested and discussed.

It is shown how convolution kernels for efficient spatio-temporal filtering can be implemented in practical situations. The method is based on applying a set of jointly optimized filter kernels in sequence. The optimization of sequential filters is performed using a novel recursive optimization technique. A number of optimization examples are given that demonstrate the role of key parameters such as: number of kernel coefficients, number of filters in sequence, spatio-temporal and Fourier space metrics.

The sequential filtering method enables filtering using only a small fraction of the number of filter coefficients required using conventional filtering. In multidimensional filtering applications the method potentially outperforms both standard convolution and FFT based approaches by two-digit numbers.

Place, publisher, year, edition, pages
SCIA , 1999. 185-193 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21613OAI: oai:DiVA.org:liu-21613DiVA: diva2:241578
Note
Also as report LiTH-ISY-R-2142Available from: 2009-10-05 Created: 2009-10-05 Last updated: 2013-08-28

Open Access in DiVA

fulltext(150 kB)1117 downloads
File information
File name FULLTEXT01.pdfFile size 150 kBChecksum SHA-512
d27299c2ea926e367953422e27400a5c63686b6e919c41df3305b54648c3c6f084088a314a4b23a1c4e823c007702125b477cbd2c727588b33955730a79d7202
Type fulltextMimetype application/pdf

Authority records BETA

Knutsson, HansAndersson, MatsWiklund, Johan

Search in DiVA

By author/editor
Knutsson, HansAndersson, MatsWiklund, Johan
By organisation
Medical InformaticsThe Institute of TechnologyComputer Vision
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 1117 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 5396 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf