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European Conference on Computer Vision

ECCV 2000: Computer Vision — ECCV 2000 pp 273–289Cite as

Layer Extraction with a Bayesian Model of Shapes

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 1843)

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

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Author information

Authors and Affiliations

  1. Microsoft Research, 1 Guildhall St, Cambridge, CB2 3NH, UK

    P. H. S. Torr

  2. Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK

    A. R. Dick & R. Cipolla

Authors
  1. P. H. S. Torr
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  2. A. R. Dick
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  3. R. Cipolla
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Editor information

Editors and Affiliations

  1. Glenageary, Co., 5 Edwin Court, Dublin, Ireland

    David Vernon

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© 2000 Springer-Verlag Berlin Heidelberg

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Torr, P.H.S., Dick, A.R., Cipolla, R. (2000). Layer Extraction with a Bayesian Model of Shapes. In: Vernon, D. (eds) Computer Vision — ECCV 2000. ECCV 2000. Lecture Notes in Computer Science, vol 1843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45053-X_18

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  • DOI: https://doi.org/10.1007/3-540-45053-X_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67686-7

  • Online ISBN: 978-3-540-45053-5

  • eBook Packages: Springer Book Archive

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