Abstract
The presented work aims at defining techniques for the detection and localisation of objects, such as aircrafts in clutter backgrounds, on aerial or satellite images. A boosting algorithm is used to select discriminating features and a descriptor robust to background and target texture variations is introduced. Several classical descriptors have been studied and compared to the new descriptor, the HDHR. It is based on the assumption that targets and backgrounds have different textures. Image synthesis is then used to generate large amounts of learning data: the Adaboost has thus access to sufficiently representative data to take into account the variability of real operational scenes. Observed results prove that a vision system can be trained on adapted simulated data and yet be efficient on real images.
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Daugman, J.G.: Complete discrete 2-D Gabor transform by neural networks for image analysis and compression. IEEE Trans. on Acoustics, Speech and Signal Processing 36(7), 1169–1179 (1988)
Dorko, G., Schmid, C.: Selection of Scale-Invariant Parts for Object Class Recognition. In: Proc. ICCV, Nice, pp. 634–639 (2003)
Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: Proc. CVPR, Madison, vol. 2, pp. 264–271 (2003)
Freeman, W.T., Adelson, E.H.: The design and use of steerable filters. IEEE Trans. PAMI 13(9), 891–906 (1991)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and application to boosting. J. of Comp. And Sys. Sc. 55(1), 119–139 (1997)
Hsieh, J.-W., Chen, J.-M., Chuang, C.-H., Fan, K.-C.: Aircraft type recognition in satellite images. IEEE Proc.-Vis. Image Signal Process 152(3) (June 2005)
Lazebnik, S., Schmid, C., Ponce, J.: Affine invariant local descriptors and neighborhood statistics for texture recognition. In: Proc ICCV, Nice, vol. 2, pp. 914–921 (2003)
Leibe, B., Schiele, B.: Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search, pp. 145–153. DAGM, Tübingen (2004)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. ICCV, Corfu. pp. 1150–1157 (1999)
Mostafa, M.G., Hemayed, E.E., Farag, A.A.: Target recognition via 3D object reconstruction from image sequence and contour matching. Pattern Recognition Letters 20, 1381–1387 (1999)
Constantine, P.: Papageorgiou, Michael Oren, and Tomaso Poggio, A general framework for object detection. In: Proc. ICCV, Bombay, pp. 555–562 (1998)
Porikli, F.M.: Integral histogram: A fast way to extract histograms in Cartesian spaces. In: Proc. CVPR, San Diego, pp. 829–836 (2005)
Ruch, O., Dufour, J.: Real-time automatic target recognition and identification of ground vehicles for airborne optronic systems. In: Proceedings of the SPIE, vol. 5909, pp. 11–20 (2005)
Stefan, K., Georg, P., Helmut, G., Horst, B., Joachim, B.: A 3D Teacher for Car Detection in Aerial Images. In: Proceedings of the Eleventh IEEE International Conference on Computer Vision, Workshop on 3D Representation for Recognition (3dRR-2007) (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, Hawaii, pp. 511–518 (2001)
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Perrotton, X., Sturzel, M., Roux, M. (2008). Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_29
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DOI: https://doi.org/10.1007/978-3-540-79547-6_29
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