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
Automatic Estimation of Epipolar Geometry from Blob Features
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-5698-5983
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
2004 (English)Report (Other academic)
Abstract [en]

This report describes how blob features can be used for automatic estimation of the fundamental matrix from two perspective projections of a 3D scene. Blobs are perceptually salient, homogeneous, compact image regions. They are represented by their average colour, area, centre of gravity and inertia matrix. Coarse blob correspondences are found by voting using colour and local similarity transform matching on blob pairs. We then do RANSAC sampling of the coarse correspondences, and weight each estimate according to how well the approximating conics and colours of two blobs correspond. The initial voting significantly reduces the number of RANSAC samples required, and the extra information besides position, allows us to reject false matches more accurately than in RANSAC using point features.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 2004. , 11 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2620
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-53358ISRN: LiTH-ISY-R-2620OAI: oai:DiVA.org:liu-53358DiVA: diva2:288340
Available from: 2010-01-20 Created: 2010-01-20 Last updated: 2015-12-10Bibliographically approved

Open Access in DiVA

fulltext(3260 kB)300 downloads
File information
File name FULLTEXT01.pdfFile size 3260 kBChecksum SHA-512
a442a12d3f279c8dc085c300a6ae2c54d03ac5f53595a5aebee1803c7f05c1ef7c24c21b8b676c92b63c6fd0ffa2f595f2f14c0920625f425752568fd8d014a0
Type fulltextMimetype application/pdf

Authority records BETA

Forssen, Per-ErikMoe, Anders

Search in DiVA

By author/editor
Forssen, Per-ErikMoe, Anders
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 300 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: 272 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