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A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery

doi: 10.6062/jcis.2015.06.03.0101

(Free PDF)

Authors

Juliano E. C. Cruz, Elcio H. Shiguemori and Lamartine N. F. Guimarães

Abstract

In this article, the three most used object detection approaches, Linear Binary Pattern cascade, Haarlike cascade, and Histogram of Oriented Gradients with Support Vector Machine are applied to automatic runway detection in high resolution satellite imagery and their results are compared. They have been employed predominantly for human feature recognition and this paper tests theirs applicability to runways. The results show that they can be indeed employed for this purpose with LBP and Haar approaches performing better than HOG+SVM.

Keywords

Runway detection, satellite imagery, boosting, boosted cascade, LBP, Haar-like, HOG, SVM, com- putational mathematics.

References

[1] E. Corvee and F. Bremond Haar like and LBP based features for face, head and people detection in video sequences, International Workshop on Behaviour Analysis and Video Understanding, 10, 2011.

[2] Q. Chen, N. D. Georganas, and E. M. Petriu, Real-time vision-based hand gesture recognition using haar-like features, IEEE Instrumentation and Measurement Technology Conference, 1-6, 2007. doi:10.1109/IMTC.2007.379068

[3] K. Nonami, F. Kendoul, S. Suzuki, W.Wang, and D. Nakazawa, Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles, Springer, 2010.

[4] R. Rodrigues, H. Shiguemori, C. Forster, and S. Pellegrino, Color and texture features for landmarks recognition on UAV navigation, Simpósio Brasileiro de Sensoriamento Remoto, 2009.

[5] P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 511-518, 2001. doi:10.1109/CVPR.2001.990517.

[6] JT. Ahonen, A. Hadid, and M. Pietikäinen, Face recognition with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037-2041, 2004. doi:10.1109/TPAMI.2006.244.

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