Towards Intelligent Mission Profiles of Micro Air Vehicles: Multiscale Viterbi Classification

  • Sinisa Todorovic
  • Michael C. Nechyba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)


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.


Object Recognition Image Class Aerial Image Visual Context Conditional Probability Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)zbMATHGoogle Scholar
  5. 5.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufamnn, San Mateo (1988)Google Scholar
  6. 6.
    McLachlan, G.J., Thriyambakam, K.T.: The EM algorithm and extensions. John Wiley & Sons, Chichester (1996)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Cheng, H., Bouman, C.A.: Multiscale bayesian segmentation using a trainable context model. IEEE Trans. Image Processing 10 (2001)Google Scholar
  9. 9.
    Choi, H., Baraniuk, R.G.: Multiscale image segmentation using wavelet-domain Hidden Markov Models. IEEE Trans. Image Processing 10 (2001)Google Scholar
  10. 10.
    Cheng, H.D., Jiang, X.H., Sun, Y., Jingli, W.: Color image segmentation: advances and prospects. Pattern Recognition 34 (2001)Google Scholar
  11. 11.
    Randen, T., Husoy, H.: Filtering for texture classification:A comparative study. IEEE Trans. Pattern Analysis Machine Intelligence 21 (1999)Google Scholar
  12. 12.
    Kingsbury, N.: Image processing with complex wavelets. Phil. Trans. Royal Soc. London 357 (1999)Google Scholar
  13. 13.
    Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, London (2001)Google Scholar
  14. 14.
    Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using Hidden Markov Models. IEEE Trans. Signal Processing 46 (1998)Google Scholar
  15. 15.
    Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Processing 3 (1994)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    Aitkin, M., Rubin, D.B.: Estimation and hypothesis testing in finite mixture models. J. Royal Stat. Soc. B-47 (1985)Google Scholar
  18. 18.
    Frey, B.J.: Graphical Models for Machine Learning and Digital Communication. The MIT Press, Cambridge (1998)Google Scholar
  19. 19.
    Storkey, A.J., Williams, C.K.I.: Image modeling with position-encoding dynamic trees. IEEE Trans. Pattern Analysis Machine Intelligence 25 (2003)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. on Communications COM-28 (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sinisa Todorovic
    • 1
  • Michael C. Nechyba
    • 1
  1. 1.ECE DepartmentUniversity of FloridaGainesvilleUSA

Personalised recommendations