A Distributed and Collective Approach for Curved Object-Based Range Image Segmentation

  • Smaine Mazouzi
  • Zahia Guessoum
  • Fabien Michel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

In this paper, we use multi-agent paradigm in order to propose a new method of image segmentation. The images considered in this work are the range images which can contain at once polyhedral and curved objects. The proposed method uses a multi-agent approach where agents align the region borders to the surrounding surfaces which make emerging a collective segmentation of the image. The agents move on the image and when they arrive on the pixels of a region border they align these pixels to their respective surfaces. The resulting competitive alignment allows at once the emergence of the image edges and the disappearance of the noise regions. The test results obtained with real images show a good potential of the new method for accurate image segmentation.

Keywords

Image segmentation Multi-agent systems Curved Object Range image 

References

  1. 1.
    Ballet, P., Rodin, V., Tisseau, J.: Edge detection using a multiagent system. In: 10th Scandinavian Conference on Image Analysis, Lapeenranta, Finland, pp. 621–626 (1997)Google Scholar
  2. 2.
    Ferber, J.: Les systèmes multi-agents : vers une intelligence collective. Informatique, Intelligence Artificielle. Inter Éditions (1995)Google Scholar
  3. 3.
    Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K.W., Eggert, D.W., Fitzgibbon, A.W., Fisher, R.B.: An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(7), 673–689 (1996)CrossRefGoogle Scholar
  4. 4.
    Jiang, X., Bunke, H.: Edge detection in range images based on Scan Line approximation. Computer Vision and Image Understanding 73(2), 183–199 (1999)CrossRefGoogle Scholar
  5. 5.
    Jiang, X.: Recent advances in range image segmentation. In: Selected Papers from the International Workshop on Sensor Based Intelligent Robots, London, UK, pp. 272–286. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Jones, J., Saeed, M.: Image enhancement, an emergent pattern formation approach via decentralised multi-agent systems. Multiagent and Grid Systems Journal (ISO Press) Special Issue on Nature inspired systems for parallel, asynchronous and decentralised environments 3(1), 105–140 (2007)MATHGoogle Scholar
  7. 7.
    Liu, J., Tang, Y.Y.: Adaptive image segmentation with distributed behavior-based agents. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(6), 544–551 (1999)CrossRefGoogle Scholar
  8. 8.
    Mazouzi, S., Batouche, M.: A new bayesian method for range image segmentation. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 453–466. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Richard, N., Dojat, M., Garbay, C.: Automated segmentation of human brain MR images using a multi-agent approach. Artificial Intelligence in Medicine 30(2), 153–176 (2004)CrossRefGoogle Scholar
  10. 10.
    Rodin, V., Benzinou, A., Guillaud, A., Ballet, P., Harrouet, F., Tisseau, J., Le Bihan, J.: An immune oriented multi-agent system for biological image processing. Pattern Recognition 37(4), 631–645 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Smaine Mazouzi
    • 1
  • Zahia Guessoum
    • 2
  • Fabien Michel
    • 3
  1. 1.Dép. d’informatiqueUniversité de SkikdaAlgérie
  2. 2.LIP6Université de Paris 6ParisFrance
  3. 3.LIRMMMontpellier Cedex 5France

Personalised recommendations