AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES

Chun Liu

Abstract

Recently image based facade modeling has attracted significant attention for 3D urban reconstruction because of low cost on data acquisition and large amount of available image processing tools. In image based facade modeling, it normally requests a rectified and segmented image input covering only the facade region. Yet this requirement involves heavy manual work on the perspective rectification and facade region extraction. In this paper, we propose an automatic rectification and segmentation process using line segment features. The raw input image is firstly rectified with the help of two vanishing points estimated from line segment in the image. Then based on the line segment spatial distribution and the luminance feature, the facade region is extracted from the sky, the road and the near-by buildings. The experiments show this method successfully work on Paris urban buildings.

References

  1. Grompone von Gioi, R., Jakubowicz, J., Morel, J. M., and Randall, G. (2010). LSD: A Fast Line Segment Detector with a False Detection Control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(4):722-732.
  2. Kalantaria, M., Jung, F., Paparoditis, N., and Guedon, J. (2008). Robust and automatic vanishing points detection with their uncertainties from a single uncalibrated image, by planes extraction on the unit sphere. In ISPRS08, page B3a: 203 ff.
  3. Laungrungthip, N. (2008). Sky detection in images for solar exposure prediction.
  4. Laungrungthip, N., McKinnon, A., Churcher, C., and Unsworth, K. (2008). Edge-based detection of sky regions in images for solar exposure prediction. In IVCNZ08, pages 1-6.
  5. Liebowitz, D. and Zisserman, A. (1998). Metric rectification for perspective images of planes. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 7898, pages 482-, Washington, DC, USA. IEEE Computer Society.
  6. Martinez, W. (2001). Computational Statistics Handbook with Matlab. CRC Press, Boca Raton.
  7. Matas, J., Galambos, C., and Kittler, J. (1998). Progressive probabilistic hough transform.
  8. Quartulli, M. and Datcu, M. (2004). Stochastic Geometrical Modeling for Built-Up Area Understanding From a Single SAR Intensity Image With Meter Resolution. IEEE Transactions on Geoscience and Remote Sensing, 42:1996-2003.
  9. Schmitt, F. and Priese, L. (2009). Sky detection in cscsegmented color images. In VISAPP (2), pages 101- 106.
  10. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C. (2008). A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell., 30:1068-1080.
  11. Zafarifar, B. and de With, P. (2006). Blue Sky Detection for Picture Quality Enhancement. pages 522-532.
Download


Paper Citation


in Harvard Style

Liu C. (2011). AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 104-111. DOI: 10.5220/0003316401040111


in Bibtex Style

@conference{visapp11,
author={Chun Liu},
title={AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={104-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003316401040111},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - AUTOMATIC FACADE IMAGE RECTIFICATION AND EXTRACTION USING LINE SEGMENT FEATURES
SN - 978-989-8425-47-8
AU - Liu C.
PY - 2011
SP - 104
EP - 111
DO - 10.5220/0003316401040111