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Statistical Foreground Modelling for Object Localisation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2000 (English)Conference paper, Published paper (Refereed)
Abstract [en]

A Bayesian approach to object localisation is feasible given suitable likelihood models for image observations. Such a likelihood involves statistical modelling - and learning - both of the object foreground and of the scene background. Statistical background models are already quite well understood. Here we propose a “conditioned likelihood” model for the foreground, conditioned on variations both in object appearance and illumination. Its effectiveness in localising a variety of objects is demonstrated.

Place, publisher, year, edition, pages
2000. 307-323 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-69635DOI: 10.1007/3-540-45053-X_20OAI: oai:DiVA.org:kth-69635DiVA: diva2:485657
Conference
Proceedings of the European Conference on Computer Vision (ECCV 2000)
Note
NR 20140805Available from: 2012-01-29 Created: 2012-01-29Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Language
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