Layer extraction with a bayesian model of shapes
Date
2000
Authors
Torr, P.
Dick, A.R.
Cipolla, R.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2000, vol.1843, pp.273-289
Statement of Responsibility
P. H. S. Torr, A. R. Dick and R. Cipolla
Conference Name
European Conference on Computer Vision (ECCV) (26 Jun 2000 - 1 Jul 2000 : Dublin)
Abstract
This paper describes an automatic 3D surface modelling system that extracts dense 3D surfaces from uncalibrated video sequences. In order to extract this 3D model the scene is represented as a collection of layers and a new method for layer extraction is described. The new segmentation method differs from previous methods in that it uses a specific prior model for layer shape. A probabilistic hierarchical model of layer shape is constructed, which assigns a density function to the shape and spatial relationships between layers. This allows accurate and efficient algorithms to be used when finding the best segmentation. Here this framework is applied to architectural scenes, in which layers commonly correspond to windows or doors and hence belong to a tightly constrained family of shapes.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© Springer-Verlag Berlin Heidelberg 2000