[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Engineering of Computer Vision Algorithms Using Evolutionary Algorithms

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5807))

Abstract

Computer vision algorithms are currently developed by looking up the available operators from the literature and then arranging those operators such that the desired task is performed. This is often a tedious process which also involves testing the algorithm with different lighting conditions or at different sites. We have developed a system for the automatic generation of computer vision algorithms at interactive frame rates using GPU accelerated image processing. The user simply tells the system which object should be detected in an image sequence. Simulated evolution, in particular Genetic Programming, is used to automatically generate and test alternative computer vision algorithms. Only the best algorithms survive and eventually provide a solution to the user’s image processing task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2007)

    MATH  Google Scholar 

  2. Koza, J.R.: Genetic Programming. In: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    Google Scholar 

  3. 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)

    Book  MATH  Google Scholar 

  4. Koza, J.R., Bennett III, F.H., Andre, D., Keane, M.A.: Genetic Programming III. Darwinian Invention and Problem Solving. Morgan Kaufmann Publishers, San Francisco (1999)

    MATH  Google Scholar 

  5. Linden, D.S.: Innovative antenna design using genetic algorithms. In: Bentley, P.J., Corne, D.W. (eds.) Creative Evolutionary Systems, pp. 487–510. Morgan Kaufmann, San Francisco (2002)

    Chapter  Google Scholar 

  6. Koza, J.R., Al-Sakran, S.H., Jones, L.W.: Automated re-invention of six patented optical lens systems using genetic programming. In: Proc. of the 2005 Conf. on Genetic and Evolutionary Computation, pp. 1953–1960. ACM, New York (2005)

    Google Scholar 

  7. Lohmann, R.: Bionische Verfahren zur Entwicklung visueller Systeme. PhD thesis, Technische Universität Berlin, Verfahrenstechnik und Energietechnik (1991)

    Google Scholar 

  8. 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, Proc. of the 1st Annual Conf., pp. 309–314. The MIT Press, Cambridge (1996)

    Google Scholar 

  9. 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, pp. 108–118. Evolutionary Programming Society (1993)

    Google Scholar 

  10. Andre, D.: Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 477–494. The MIT Press, Cambridge (1994)

    Google Scholar 

  11. Ebner, M.: On the evolution of interest operators using genetic programming. In: Poli, R., Langdon, W.B., Schoenauer, M., Fogarty, T., Banzhaf, W. (eds.) Late Breaking Papers at EuroGP 1998: the 1st European Workshop on Genetic Programming, Paris, France, pp. 6–10. The University of Birmingham, UK (1998)

    Google Scholar 

  12. Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. on Pattern Analysis and Machine Intelligence 16(9), 901–905 (1994)

    Article  Google Scholar 

  13. Katz, A.J., Thrift, P.R.: Generating image filters for target recognition by genetic learning. IEEE Trans. on Pattern Analysis and Machine Int. 16(9), 906–910 (1994)

    Article  Google Scholar 

  14. 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)

    Chapter  Google Scholar 

  15. 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., Stanford University, pp. 363–368. The MIT Press, Cambridge (1996)

    Google Scholar 

  16. 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 

  17. 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 

  18. 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 

  19. 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 Int. Congress on Evolutionary Computation, San Francisco, CA, pp. 1316–1323. IEEE, Los Alamitos (2006)

    Google Scholar 

  20. Koza, J.R.: Artificial life: Spontaneous emergence of self-replicating and evolutionary self-improving computer programs. In: Langton, C.G. (ed.) Artificial Life III: SFI Studies in the Sciences of Complexity Proc., vol. XVII, pp. 225–262. Addison-Wesley, Reading (1994)

    Google Scholar 

  21. Nordin, P.: A compiling genetic programming system that directly manipulates the machine code. In: Kinnear Jr., K.E. (ed.) Advances in Genetic Programming, pp. 311–331. The MIT Press, Cambridge (1994)

    Google Scholar 

  22. Miller, J.F.: An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Banzhaf, W., et al. (eds.) Proc. of the Genetic and Evolutionary Computation Conf., pp. 1135–1142. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  23. 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 

  24. Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs: Stream computing on graphics hardware. In: Int. Conf. on Comp. Graphics and Interactive Techniques (ACM SIGGRAPH), pp. 777–786 (2004)

    Google Scholar 

  25. NVIDIA: NVIDIA CUDA. Compute Unified Device Architecture. V1.1 (2007)

    Google Scholar 

  26. 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)

    Chapter  Google Scholar 

  27. Yang, R., Pollefeys, M.: Multi-resolution real-time stereo on commodity graphics hardware. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 211–218. IEEE, Los Alamitos (2003)

    Google Scholar 

  28. Yang, R., Pollefeys, M.: A versatile stereo implementation on commodity graphics hardware. Real-Time Imaging 11(1), 7–18 (2005)

    Article  Google Scholar 

  29. Fung, J., Mann, S.: Computer vision signal processing on graphics processing units. In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 2004, vol. 5, pp. 93–96. IEEE, Los Alamitos (2004)

    Google Scholar 

  30. Fung, J., Mann, S., Aimone, C.: OpenVIDIA: Parallel GPU computer vision. In: Proc. of the 13th annual ACM Int. Conf. on Multimedia, Singapore, pp. 849–852. ACM, New York (2005)

    Chapter  Google Scholar 

  31. Akenine-Möller, T., Haines, E.: Real-Time Rendering, 2nd edn. A K Peters, Natick (2002)

    Google Scholar 

  32. Fernando, R., Kilgard, M.J.: The Cg Tutorial. In: The Definitive Guide to Programmable Real-Time Graphics. Addison-Wesley, Boston (2003)

    Google Scholar 

  33. Rost, R.J.: OpenGL Shading Language, 2nd edn. Addison-Wesley, Upper Saddle River (2006)

    Google Scholar 

  34. Ebner, M.: A real-time evolutionary object recognition system. In: Vanneschi, L., Gustafson, S., Moraglio, A., Falco, I.D., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 268–279. Springer, Heidelberg (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ebner, M. (2009). Engineering of Computer Vision Algorithms Using Evolutionary Algorithms. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04697-1_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04696-4

  • Online ISBN: 978-3-642-04697-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics