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
Depth Segmentation and Occluded Scene Reconstruction using Ego-motion
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: 0000-0002-9091-4724
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
1998 (English)In: Proceedings of the SPIE Conference on Visual Information Processing: Orlando, Florida, USA, 1998, 112-123 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a signal processing strategy for depth segmentation and scene reconstruction that incorporates occlusion as a natural component. The work aims to maximize the use of connectivity in the temporal domain as much as possible under the condition that the scene is static and that the camera motion is known. An object behind the foreground is reconstructed using the fact that different parts of the object have been seen in different images in the sequence. One of the main ideas in this paper is the use of a spatio- temporal certainty volume c(x) with the same dimension as the input spatio- temporal volume s(x), and then use c(x) as a 'blackboard' for rejecting already segmented image structures. The segmentation starts with searching for image structures in the foreground, eliminate their occluding influence, and then proceed. Normalized convolution, which is a Weighted Least Mean Square technique for filtering data with varying spatial reliability, is used for all filtering. High spatial resolution near object borders is achieved and only neighboring structures with similar depth supports each other.

Place, publisher, year, edition, pages
1998. 112-123 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-21721OAI: oai:DiVA.org:liu-21721DiVA: diva2:273841
Available from: 2009-10-25 Created: 2009-10-05 Last updated: 2013-08-28

Open Access in DiVA

No full text

Authority records BETA

Ulvklo, MorganKnutsson, HansGranlund, Gösta H.

Search in DiVA

By author/editor
Ulvklo, MorganKnutsson, HansGranlund, Gösta H.
By organisation
Computer VisionThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 511 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