Change search
ReferencesLink to record
Permanent link

Direct link
Statistical Foreground Modelling for Object Localisation
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2000 (English)Conference 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
URN: urn:nbn:se:kth:diva-69635DOI: 10.1007/3-540-45053-X_20OAI: diva2:485657
Proceedings of the European Conference on Computer Vision (ECCV 2000)
NR 20140805Available from: 2012-01-29 Created: 2012-01-29Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Sullivan, Josephine
By organisation
Computer Vision and Active Perception, CVAP
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
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

Altmetric score

Total: 6 hits
ReferencesLink to record
Permanent link

Direct link