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|Title:||Layer extraction with a bayesian model of shapes|
|Citation:||Computer Vision - ECCV 2000: 6th European Conference on Computer Vision. Proceedings, Part II, 2000 / vol.1843, pp.273-289|
|Series/Report no.:||Lecture notes in computer science ; Vol. 1843|
|Conference Name:||European Conference on Computer Vision (ECCV) (26 Jun 2000 - 01 Jul 2000 : Dublin)|
|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|
|Appears in Collections:||Australian Institute for Machine Learning publications|
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