Detection and Classification of Facades

Panagiotis Panagiotopoulos, Anastasios Delopoulos

2013

Abstract

This paper presents a framework that exploits the expressive power of probabilistic geometric grammars to cope with the task of facade classification. In particular, we work on a dataset of rectified facades and we attempt to discover the origin of a number of query facade segments, contaminated with noise. The building block of our description are the windows of the facade. To this direction we develop an algorithm that achieves to accurately detect them. Our core contribution though, lies on the probabilistic manipulation of the geometry of the detected windows. In particular, we propose a simple probabilistic grammar to model this geometry and we propose a methodology for learning the parameters of the grammar from a single instance of each facade through a MAP estimation procedure. The produced generative model is essentially a detector of the particular facade. After producing one model per facade in our dataset, we proceed with the classification of the query segments. Promising results indicate that the simultaneous use of an appearance model together with our geometric formulation always achieved superior classification rates than the exclusive use of the appearance model itself, justifying the value of probabilistic geometric grammars for the task of facade classification.

Paper Citation

in Harvard Style

Panagiotopoulos P. and Delopoulos A. (2013). Detection and Classification of Facades . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 641-650. DOI: 10.5220/0004283506410650

in Bibtex Style

@conference{visapp13,
author={Panagiotis Panagiotopoulos and Anastasios Delopoulos},
title={Detection and Classification of Facades},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={641-650},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004283506410650},
isbn={978-989-8565-47-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Detection and Classification of Facades
SN - 978-989-8565-47-1
AU - Panagiotopoulos P.
AU - Delopoulos A.
PY - 2013
SP - 641
EP - 650
DO - 10.5220/0004283506410650