Street Detection with Asymmetric Haar Features

  • Geovany A. Ramirez
  • Olac Fuentes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

We present a system for object detection applied to street detection in satellite images. Our system is based on asymmetric Haar features. Asymmetric Haar features provide a rich feature space, which allows to build classifiers that are accurate and much simpler than those obtained with other features. The extremely large parameter space of potential features is explored using a genetic algorithm. Our system uses specialized detectors in different street orientations that are built using AdaBoost and the C4.5 rule induction algorithm. Experimental results show that Asymmetric Haar features are better than basic Haar features for street detection.

Keywords

Object Detection Asymmetric Haar Features Machine Learning Street Detection 

References

  1. 1.
    Christophe, E., Inglada, J.: Robust road extraction for high resolution satellite images. In: IEEE International Conference on Image Processing, ICIP 2007, October 16-19, vol. 5, pp. V-437–V-440 (2007)Google Scholar
  2. 2.
    Doucette, P., Agouris, P., Stefanidis, A., Musavi, M.: Self-organised clustering for road extraction in classified imagery. ISPRS Journal of photogrammetry and Remote Sensing 55(5-6) (2001)Google Scholar
  3. 3.
    Gruen, A., Li, H.: Road extraction from aerial and satellite images by dynamic programming. ISPRS Journal of Photogrammetry and Remote Sensing 50(4), 11–20 (1995)CrossRefGoogle Scholar
  4. 4.
    Guo, X., Dean, D., Denman, S., Fookes, C., Sridharan, S.: Evaluating automatic road detection across a large aerial imagery collection. In: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 140–145 (December 2011)Google Scholar
  5. 5.
    Kuri-Morales, A.F.: Efficient compression from non-ergodic sources with genetic algorithms. In: Fourth Mexican International Conference on Computer Science, pp. 324–329 (2003)Google Scholar
  6. 6.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)Google Scholar
  7. 7.
    Papageorgiou, C.P., Oren, M., Poggio, T.: A general framework for object detection. In: ICCV 1998: Proceedings of the Sixth International Conference on Computer Vision, p. 555. IEEE Computer Society, Washington, DC (1998)Google Scholar
  8. 8.
    Péteri, R., Ranchin, T.: Multiresolution snakes for urban road extraction from ikonos and quickbird images. In: 23rd EARSeL Symposium Remote Sensing in Transition (2003)Google Scholar
  9. 9.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  10. 10.
    Ramirez, G.A., Fuentes, O.: Multi-pose face detection with asymmetric haar features. In: IEEE Workshop on Applications of Computer Vision, WACV 2008, pp. 1–6 (January 2008)Google Scholar
  11. 11.
    Tieu, K., Viola, P.: Boosting image retrieval. International Journal of Computer Vision 56(1-2), 17–36 (2004)CrossRefGoogle Scholar
  12. 12.
    Tupin, F., Houshmand, B., Datcu, M.: Road detection in dense urban areas using sar imagery and the usefulness of multiple views. IEEE Trans. Geosci. and Remote Sensing 40(11) (2002)Google Scholar
  13. 13.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of 2001 IEEE International Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  14. 14.
    Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63(2), 153–161 (2005)CrossRefGoogle Scholar
  15. 15.
    Vosselman, G., Knecht, J.: Road tracing by profile matching and kalman filtering. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images (1995)Google Scholar
  16. 16.
    Yan, D., Zhao, Z.: Road detection from quickbird fused image using ihs transform and morphology. In: IEEE International Geoscience and Remote Sensing Symposium (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geovany A. Ramirez
    • 1
  • Olac Fuentes
    • 1
  1. 1.Computer Science DepartmentUniversity of Texas at El PasoUSA

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