Abstract
Mid-level processes in vision are understood to produce structured descriptions of images without relying on very specific semantic scene knowledge. Automatic building extraction can use geometric models to a large extent. Geometric hypotheses may be inferred from the given data in 2D or 3D and represent elementary constraints as incidence or collinearity or more specific relations as symmetries. The inferred hypothesis may lead to difficulties during spatial inference due to noise and to inconsistent and mutually dependent constraints. The paper discusses the selection of mutually not-contradicting constraints via robust estimation and the selection of a set of independent constraints as a prerequisite for an optimal estimation of the objects shape. Examples from the analysis of image and range data are given.
Ascona Workshop on ‘Automatic Extraction of Man-Made Objects froma Aerial and Space Images’, 1995
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© 1995 Birkhäuser Verlag Basel
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Förstner, W. (1995). Mid-Level Vision Processes for Automatic Building Extraction. In: Gruen, A., Kuebler, O., Agouris, P. (eds) Automatic Extraction of Man-Made Objects from Aerial and Space Images. Monte Verità. Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-9242-1_17
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DOI: https://doi.org/10.1007/978-3-0348-9242-1_17
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-0348-9958-1
Online ISBN: 978-3-0348-9242-1
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