Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117097
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Type: Conference paper
Title: Layer extraction with a bayesian model of shapes
Author: Torr, P.
Dick, A.
Cipolla, R.
Citation: Computer Vision - ECCV 2000: 6th European Conference on Computer Vision. Proceedings, Part II, 2000 / vol.1843, pp.273-289
Publisher: Springer
Issue Date: 2000
Series/Report no.: Lecture notes in computer science ; Vol. 1843
ISBN: 3540676864
ISSN: 0302-9743
1611-3349
Conference Name: European Conference on Computer Vision (ECCV) (26 Jun 2000 - 01 Jul 2000 : Dublin)
Statement of
Responsibility: 
P. H. S. Torr, A. R. Dick and R. Cipolla
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.
Keywords: Structure from motion; grouping and segmentation
Rights: © Springer-Verlag Berlin Heidelberg 2000
RMID: 0030091878
DOI: 10.1007/3-540-45053-X_18
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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