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
Motion-based segmentation of image sequences
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
1996 (English)Independent thesis Advanced level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.

Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints.

The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld.

It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.

Place, publisher, year, edition, pages
1996. , 34 p.
Series
LiTH-ISY-Ex, 1596
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-54351ISRN: n/aOAI: oai:DiVA.org:liu-54351DiVA: diva2:302971
Presentation
(English)
Uppsok
Technology
Available from: 2010-03-10 Created: 2010-03-10 Last updated: 2010-03-30Bibliographically approved

Open Access in DiVA

fulltext(436 kB)293 downloads
File information
File name FULLTEXT01.pdfFile size 436 kBChecksum SHA-512
4cfeac20c09115b394fa76958adc2a45142fc081cbbf84357631caa131bc19efdf0f84abf56ee77b90ac8cc4c9fdac3be8a536fdd62f6d7abfba56d43e60e787
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Farnebäck, Gunnar
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 293 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: 317 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