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Learning Visual Operators from Examples: A New Paradigm in Image Processing
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
1999 (English)In: Proceedings of the 10th International Conference on Image Analysis and Processing (ICIAP'99): Venice, Italy, 1999Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a general strategy for designing efficient visual operators. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g. local shift invariant orientation operators and image content invariant disparity operators. Interesting similarities to biological vision functions are observed.

Place, publisher, year, edition, pages
1999.
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Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21680OAI: oai:DiVA.org:liu-21680DiVA: diva2:246039
Note
Also as Technical Report LiTH-ISY-R-2252Available from: 2009-10-08 Created: 2009-10-05 Last updated: 2014-10-08

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fulltext(316 kB)267 downloads
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Knutsson, HansBorga, Magnus

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CiteExportLink to record
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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