Abstract
In this paper an agent-based segmentation approach is presented and evaluated. The approach consists in using a high number of autonomous agents for the segmentation of a range image in its different planar regions. The moving agents perform cooperative and competitive actions on the image pixels allowing a robust extraction of regions and an accurate edge detection. An artificial potential field, created around pixels of interest, allows the agents to be gathered around edges and noise regions. The results obtained with real images are compared to those of some typical methods for range image segmentation. The comparison results show the potential of the proposed approach for scene understanding in range images regarding both segmentation efficiency, and detection accuracy.
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References
Hadiashar, A.B., Gheissari, N.: Range image segmentation using surface selection criterion. IEEE Transactions on Image Processing 15(7), 2006–2018 (2006)
Bovenkamp, E.G.P., Dijkstra, J., Bosch, J.G., Reiber, J.H.C.: Multi-agent segmentation of IVUS images. Pattern Recognition 37(4), 647–663 (2004)
Ding, Y., Ping, X., Hu, M., Wang, D.: Range image segmentation based on randomized hough transform. Pattern Recognition Letters 26(13), 2033–2041 (2005)
Fan, T.J., Medioni, G.G., Nevatia, R.: Segmented description of 3-D surfaces. IEEE Journal of Robotics and Automation 3(6), 527–538 (1987)
Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1999)
Harrouet, F., Tisseau, J., Reignier, P., Chevaillier, P.: oRis: un environnement de simulation interactive multi-agents. Technique et Science Informatiques 21(4), 499–524 (2002)
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)
Inokuchi, S., Nita, T., Matsuda, F., Sakurai, Y.: A three dimensional edge-region operator for range pictures. In: 6th International Conference on Pattern Recognition, Munich, pp. 918–920 (1982)
Jiang, X., Bowyer, K.W., Morioka, Y., Hiura, S., Sato, K., Inokuchi, S., Bock, M., Guerra, C., Loke, R.E., Hans du Buf, J.M.: Some further results of experimental comparison of range image segmentation algorithms. In: 15th International Conference on Pattern Recognition, Barcelona, Spain, vol. 4, pp. 4877–4882 (2000)
Jiang, X., Bunke, H.: Edge detection in range images based on Scan Line approximation. Computer Vision and Image Understanding 73(2), 183–199 (1999)
Kang, S.B., Ikeuchi, K.: The complex EGI: A new representation for 3-D pose determination. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(7), 707–721 (1993)
Krishnamurthy, E.V., Murthy, V.K.: Distributed agent paradigm for soft and hard computation. Journal of Network and Computer Applications 29(2), 124–146 (2006)
Li, S., Zhao, D.: Gradient-based polyhedral segmentation for range images. Pattern Recognition Letters 24(12), 2069–2077 (2003)
Li, S.Z.: Roof-edge preserving image smoothing based on MRFs. IEEE Transactions on Image Processing 9(6), 1134–1138 (2000)
Li, S.Z.: Markov random field modeling in image analysis. Springer, New York, Inc., Secaucus, NJ, USA (2001)
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)
Newell, A.: Unified theories of cognition. Harvard University Press, Cambridge, MA, USA (1990)
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)
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)
Simonin, O.: Construction of numerical potential fields with reactive agents. In: AAMAS 2005: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pp. 1351–1352. ACM Press, New York (2005)
Tsuji, T., Tanaka, Y., Morasso, P., Sanguineti, V., Kaneko, M.: Bio-mimetic trajectory generation of robots via artificial potential field with time base generator. IEEE Transactions on Systems, Man, and Cybernetics, Part C 32(4), 426–439 (2002)
Yang, H.S., Kak, A.C.: Determination of the identity, position and orientation of the topmost object in a pile. Computer Vision, Graphics, and Image Processing 36(2-3), 229–255 (1986)
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Mazouzi, S., Guessoum, Z., Michel, F., Batouche, M. (2008). An Agent-Based Approach for Range Image Segmentation. In: Jamali, N., Scerri, P., Sugawara, T. (eds) Massively Multi-Agent Technology. AAMAS 2007. Lecture Notes in Computer Science(), vol 5043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85449-4_11
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DOI: https://doi.org/10.1007/978-3-540-85449-4_11
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