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
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© 2012 Springer-Verlag Berlin Heidelberg
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Ramirez, G.A., Fuentes, O. (2012). Street Detection with Asymmetric Haar Features. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_49
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DOI: https://doi.org/10.1007/978-3-642-33275-3_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33274-6
Online ISBN: 978-3-642-33275-3
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