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
Learning Corner Orientation Using Canonical Correlation
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2001 (English)In: Proceedings of the SSAB Symposium on Image Analysis: Norrköping, 2001, 89-92 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper shows how canonical correlation can be used to learn a detector for corner orientation invariant to corner angle and intensity. Pairs of images with the same corner orientation but different angle and intensity are used as training samples. Three different image representations; intensity values, products between intensity values, and local orientation are examined. The last representation gives a well behaved result that is easy to decode into the corner orientation. To reduce dimensionality, parameters from a polynomial model fitted on the different representations is also considered. This reduction did not affect the performance of the system.

Place, publisher, year, edition, pages
2001. 89-92 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21695OAI: oai:DiVA.org:liu-21695DiVA: diva2:246077
Available from: 2009-10-08 Created: 2009-10-05 Last updated: 2014-10-08

Open Access in DiVA

fulltext(260 kB)392 downloads
File information
File name FULLTEXT01.pdfFile size 260 kBChecksum SHA-512
9b7e05f842f5c6fa245342874465a95fc34392e81948effe4e24ff2fbbf10bc2a45bcb50a51e410bc8ecfeb923f0afa33fef549acec84f75e1aeeeecf60a26a3
Type fulltextMimetype application/pdf

Authority records BETA

Johansson, BjörnBorga, MagnusKnutsson, Hans

Search in DiVA

By author/editor
Johansson, BjörnBorga, MagnusKnutsson, Hans
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 392 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: 661 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