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Proceedings Paper

Improving reconstruction of man-made objects from sensor images by machine learning
Author(s): Roman Englert; Armin B. Cremers
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Paper Abstract

In this paper we present a new approach for the acquisition and analysis of background knowledge which is used for 3D reconstruction of man-made objects -- in this case buildings. Buildings can be easily represented as parameterized graphs from which p-subisomorphic graphs will be computed. P-graphs will be defined and an upper bound complexity estimation of the computation of p-subisomorphims will be given. In order to reduce search space we will discuss several pruning mechanisms. Background knowledge requires a classification in order to receive a probability distribution which will serve as a priori knowledge for 3D building reconstruction. Therefore, we will apply an alternative view of nearest- neighbor classification to measured knowledge in order to learn based on a complete seed and a noise model a distribution of this knowledge. An application of an extensive scene consisting of 1846 building cluster which are represented as p-graphs in order to estimate a probability distribution of corner nodes demonstrates the effectiveness of our approach. An evaluation using the information coding theory determines the information gain which is provided by the estimated distribution in comparison with no available a priori knowledge.

Paper Details

Date Published: 5 August 1997
PDF: 12 pages
Proc. SPIE 3072, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III, (5 August 1997); doi: 10.1117/12.281046
Show Author Affiliations
Roman Englert, Univ. Bonn (Germany)
Armin B. Cremers, Univ. Bonn (Germany)

Published in SPIE Proceedings Vol. 3072:
Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III
David M. McKeown Jr.; J. Chris McGlone; Olivier Jamet, Editor(s)

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