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Contour-relaxed Superpixels
Goethe University, Frankfurt, Germany. (VSI Group, Computer Science Dept.)
Goethe University, Frankfurt, Germany. (VSI Group, Computer Science Dept.)
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. (CVL)
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We propose and evaluate a versatile scheme for image pre-segmentation that generates a partition of the image into a selectable number of patches (’superpixels’), under the constraint of obtaining maximum homogeneity of the ’texture’ inside of each patch, and maximum accordance of the contours with both the image content as well as a Gibbs-Markov random field model. In contrast to current state-of-the art approaches to superpixel segmentation, ’homogeneity’ does not limit itself to smooth region-internal signals and high feature value similarity between neighboring pixels, but is applicable also to highly textured scenes. The energy functional that is to be maximized for this purpose has only a very small number of design parameters, depending on the particular statistical model used for the images.

The capability of the resulting partitions to deform according to the image content can be controlled by a single parameter. We show by means of an extensive comparative experimental evaluation that the compactness-controlled contour-relaxed superpixels method outperforms the state-of-the art superpixel algorithms with respect to boundary recall and undersegmentation error while being faster or on a par with respect to runtime.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. 280-293 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8081
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-93239DOI: 10.1007/978-3-642-40395-8_21ISBN: 978-3-642-40394-1 (print)ISBN: 978-3-642-40395-8 (print)OAI: oai:DiVA.org:liu-93239DiVA: diva2:623602
Conference
EMMCVPR 2013. 9th International Conference Energy Minimization Methods in Computer Vision and Pattern Recognition, August 19-21, Lund, Sweden
Projects
ELLIIT
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2013-05-28 Created: 2013-05-28 Last updated: 2014-12-03Bibliographically approved

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Mester, Rudolf

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Total: 489 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