A Real-Time Evolutionary Object Recognition System

  • Marc Ebner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)


We have created a real-time evolutionary object recognition system. Genetic Programming is used to automatically search the space of possible computer vision programs guided through user interaction. The user selects the object to be extracted with the mouse pointer and follows it over multiple frames of a video sequence. Several different alternative algorithms are evaluated in the background for each input image. Real-time performance is achieved through the use of the GPU for image processing operations.


Genetic Program Graphic Processing Unit Input Image Good Individual Graphic Hardware 
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.


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  1. 1.
    Ebner, M.: An adaptive on-line evolutionary visual system. In: Hart, E., Paechter, B., Willies, J. (eds.) Workshop on Pervasive Adaptation, Venice, Italy. IEEE, Los Alamitos (2008) (in press) Google Scholar
  2. 2.
    Lohmann, R.: Bionische Verfahren zur Entwicklung visueller Systeme. Ph.D thesis, Technische Universität Berlin, Verfahrenstechnik und Energietechnik (1991)Google Scholar
  3. 3.
    Harris, C., Buxton, B.: Evolving edge detectors with genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996. Proc. of the 1st Annual Conf., pp. 309–314. The MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Rizki, M.M., Tamburino, L.A., Zmuda, M.A.: Evolving multi-resolution feature-detectors. In: Fogel, D.B., Atmar, W. (eds.) Proc. of the 2nd American Conf. on Evolutionary Programming, Evolutionary Programming Society, pp. 108–118 (1993)Google Scholar
  5. 5.
    Ebner, M.: On the evolution of interest operators using genetic programming. In: Poli, R., Langdon, W.B., Schoenauer, M., Fogarty, T., Banzhaf, W. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 6–10. Springer, Heidelberg (1998)Google Scholar
  6. 6.
    Katz, A.J., Thrift, P.R.: Generating image filters for target recognition by genetic learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(9), 906–910 (1994)CrossRefGoogle Scholar
  7. 7.
    Ebner, M., Zell, A.: Evolving a task specific image operator. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D.W., Smith, G.D., Fogarty, T.C. (eds.) EvoIASP 1999 and EuroEcTel 1999. LNCS, vol. 1596, pp. 74–89. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Poli, R.: Genetic programming for image analysis. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996, Proc. of the 1st Annual Conf., pp. 363–368. The MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    Johnson, M.P., Maes, P., Darrell, T.: Evolving visual routines. In: Brooks, R.A., Maes, P. (eds.) Artificial Life IV, Proc. of the 4th Int. Workshop on the Synthesis and Simulation of Living Systems, pp. 198–209. The MIT Press, Cambridge (1994)Google Scholar
  10. 10.
    Trujillo, L., Olague, G.: Synthesis of interest point detectors through genetic programming. In: Proc. of the Genetic and Evolutionary Computation Conf., Seattle, WA, pp. 887–894. ACM, New York (2006)Google Scholar
  11. 11.
    Krawiec, K., Bhanu, B.: Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation 11(5), 635–650 (2007)CrossRefGoogle Scholar
  12. 12.
    Treptow, A., Zell, A.: Combining AdaBoost learning and evolutionary search to select features for real-time object detection. In: Proc. of the IEEE Congress on Evolutionary Computation, Portland, OR, vol. 2, pp. 2107–2113. IEEE, Los Alamitos (2004)Google Scholar
  13. 13.
    Heinemann, P., Streichert, F., Sehnke, F., Zell, A.: Automatic calibration of camera to world mapping in robocup using evolutionary algorithms. In: Proc. of the IEEE International Congress on Evolutionary Computation, San Francisco, CA, pp. 1316–1323. IEEE, Los Alamitos (2006)Google Scholar
  14. 14.
    Cagnoni, S.: Evolutionary computer vision: a taxonomic tutorial. In: 8th Int. Conf. on Hybrid Intelligent Systems, pp. 1–6. IEEE Comp. Society, Los Alamitos (2008)Google Scholar
  15. 15.
    Mussi, L., Cagnoni, S.: Artificial creatures for object tracking and segmentation. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 255–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1996)zbMATHGoogle Scholar
  17. 17.
    Rechenberg, I.: Evolutionsstrategie 1994. frommann-holzboog, Stuttgart (1994)Google Scholar
  18. 18.
    Koza, J.R.: Genetic Programming. In: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  19. 19.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming - An Introduction: On The Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers, San Francisco (1998)CrossRefzbMATHGoogle Scholar
  20. 20.
    Miller, J.F.: An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proc. of the Genetic and Evolutionary Computation Conf., pp. 1135–1142. Morgan Kaufmann, San Francisco (1999)Google Scholar
  21. 21.
    Ebner, M.: Color Constancy. John Wiley & Sons, England (2007)zbMATHGoogle Scholar
  22. 22.
    Rost, R.J.: OpenGL Shading Language, 2nd edn. Addison-Wesley, Upper Saddle River (2006)Google Scholar
  23. 23.
    Fung, J., Tang, F., Mann, S.: Mediated reality using computer graphics hardware for computer vision. In: Proc. of the 6th Int. Symposium on Wearable Computers, pp. 83–89. ACM, New York (2002)CrossRefGoogle Scholar
  24. 24.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. In: Eurographics 2005, State of the Art Reports, pp. 21–51 (2005)Google Scholar
  25. 25.
    NVIDIA: Compute Unified Device Architecture. Programming Guide V.1.1 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marc Ebner
    • 1
  1. 1.Wilhelm-Schickard-Institut für InformatikEberhard-Karls-Universität TübingenTübingenGermany

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