ESTIMATING PLANAR STRUCTURE IN SINGLE IMAGES BY LEARNING FROM EXAMPLES

Osian Haines, Andrew Calway

2012

Abstract

Outdoor urban scenes typically contain many planar surfaces, which are useful for tasks such as scene reconstruction, object recognition, and navigation, especially when only a single image is available. In such situations the lack of 3D information makes finding planes difficult; but motivated by how humans use their prior knowledge to interpret new scenes with ease, we develop a method which learns from a set of training examples, in order to identify planar image regions and estimate their orientation. Because it does not rely explicitly on rectangular structures or the assumption of a ‘Manhattan world’, our method can generalise to a variety of outdoor environments. From only one image, our method reliably distinguishes planes from non-planes, and estimates their orientation accurately; this is fast and efficient, with application to a real-time system in mind.

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Paper Citation


in Harvard Style

Haines O. and Calway A. (2012). ESTIMATING PLANAR STRUCTURE IN SINGLE IMAGES BY LEARNING FROM EXAMPLES . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 289-294. DOI: 10.5220/0003708902890294

in Bibtex Style

@conference{icpram12,
author={Osian Haines and Andrew Calway},
title={ESTIMATING PLANAR STRUCTURE IN SINGLE IMAGES BY LEARNING FROM EXAMPLES},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003708902890294},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - ESTIMATING PLANAR STRUCTURE IN SINGLE IMAGES BY LEARNING FROM EXAMPLES
SN - 978-989-8425-99-7
AU - Haines O.
AU - Calway A.
PY - 2012
SP - 289
EP - 294
DO - 10.5220/0003708902890294