Abstract
In this paper, we present a vision system for object recognition in aerial images, which enables broader mission profiles for Micro Air Vehicles (MAVs). The most important factors that inform our design choices are: real-time constraints, robustness to video noise, and complexity of object appearances. As such, we first propose the HSI color space and the Complex Wavelet Transform (CWT) as a set of sufficiently discriminating features. For each feature, we then build tree-structured belief networks (TSBNs) as our underlying statistical models of object appearances. To perform object recognition, we develop the novel multiscale Viterbi classification (MSVC) algorithm, as an improvement to multiscale Bayesian classification (MSBC). Next, we show how to globally optimize MSVC with respect to the feature set, using an adaptive feature selection algorithm. Finally, we discuss context-based object recognition, where visual contexts help to disambiguate the identity of an object despite the relative poverty of scene detail in flight images, and obviate the need for an exhaustive search of objects over various scales and locations in the image. Experimental results show that the proposed system achieves smaller classification error and fewer false positives than systems using the MSBC paradigm on challenging real-world test images.
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Ettinger, S.M., Nechyba, M.C., Ifju, P.G., Waszak, M.: Vision-guided flight stability and control for Micro Air Vehicles. In: Proc. IEEE Int’l Conf. Intelligent Robots and Systems (IROS), Laussane, Switzerland (2002)
Ettinger, S.M., Nechyba, M.C., Ifju, P.G., Waszak, M.: Vision-guided flight stability and control for Micro Air Vehicles. Advanced Robotics 17 (2003)
Todorovic, S., Nechyba, M.C., Ifju, P.: Sky/ground modeling for autonomous MAVs. In: Proc. IEEE Int’l Conf. Robotics and Automation (ICRA), Taipei, Taiwan (2003)
Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufamnn, San Mateo (1988)
McLachlan, G.J., Thriyambakam, K.T.: The EM algorithm and extensions. John Wiley & Sons, Chichester (1996)
Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A.: Context-based vision system for place and object recognition. In: Proc. Int’l Conf. Computer Vision (ICCV), Nice, France (2003)
Cheng, H., Bouman, C.A.: Multiscale bayesian segmentation using a trainable context model. IEEE Trans. Image Processing 10 (2001)
Choi, H., Baraniuk, R.G.: Multiscale image segmentation using wavelet-domain Hidden Markov Models. IEEE Trans. Image Processing 10 (2001)
Cheng, H.D., Jiang, X.H., Sun, Y., Jingli, W.: Color image segmentation: advances and prospects. Pattern Recognition 34 (2001)
Randen, T., Husoy, H.: Filtering for texture classification:A comparative study. IEEE Trans. Pattern Analysis Machine Intelligence 21 (1999)
Kingsbury, N.: Image processing with complex wavelets. Phil. Trans. Royal Soc. London 357 (1999)
Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, London (2001)
Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using Hidden Markov Models. IEEE Trans. Signal Processing 46 (1998)
Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Processing 3 (1994)
Feng, X., Williams, C.K.I., Felderhof, S.N.: Combining belief networks and neural networks for scene segmentation. IEEE Trans. Pattern Analysis Machine Intelligence 24 (2002)
Aitkin, M., Rubin, D.B.: Estimation and hypothesis testing in finite mixture models. J. Royal Stat. Soc. B-47 (1985)
Frey, B.J.: Graphical Models for Machine Learning and Digital Communication. The MIT Press, Cambridge (1998)
Storkey, A.J., Williams, C.K.I.: Image modeling with position-encoding dynamic trees. IEEE Trans. Pattern Analysis Machine Intelligence 25 (2003)
Irving, W.W., Fieguth, P.W., Willsky, A.S.: An overlapping tree approach to multiscale stochastic modeling and estimation. IEEE Trans. Image Processing 6 (1997)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. on Communications COM-28 (1980)
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© 2004 Springer-Verlag Berlin Heidelberg
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Todorovic, S., Nechyba, M.C. (2004). Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24671-8_14
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DOI: https://doi.org/10.1007/978-3-540-24671-8_14
Publisher Name: Springer, Berlin, Heidelberg
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