Abstract
Laser range imaging is the current priority research areas of airborne lidar. And realizing accurate edge detection of laser range image is the key of completing the subsequent three-dimensional reconstruction. Based on the characteristics of laser range image and the deficiencies of traditional edge detection methods, a new improved fast adaptive ant colony algorithm for edge detection of laser range image has been proposed in this paper. Due to the initial cluster center and the heuristic guiding function used in the algorithm, the randomness and blindness of ants walking are eliminated thoroughly, and the speed of image edge detection is also greatly increased. Meanwhile, thanks to the applied ants’ selection mechanism and updating mechanism varying in contents, the error detection rate and omission factor of edge points as well as noise interference are all avoided, and the accuracy and adaptability of laser range image edge detection are greatly improved as well. Experimental results have shown that, this algorithm is more effective than other edge detection methods, and can meet the requirements of three-dimensional reconstruction.
Supported by the National Natural Science Foundation of China (Grant No.60672154).
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References
Newman, P., Sibley, G., Smith, M., Cummins, M., Harrison, A., Mei, C., Posner, I., Shade, R., Schroeter, D., Murphy, L., et al.: Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers. J. The International Journal of Robotics Research 28, 1406–1433 (2009)
Chevrier, C., Perrin, J.P.: Interactive parametric modeling: POG a tool the cultural heritage monument 3D reconstruction. In: CAADRIA conference, Chiang Mai (2008)
Kolomenkin, M., Shimshoni, I., Tal, A.: On edge detection on surfaces. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2767–2774 (2009)
Ren, C., Wu, S.-l., Jiao, L.-c.: Edge Detection Algorithm of SAR Images with Wedgelet Filter. J. Journal of Beijing Institute of Technology 3, 346–350 (2008)
Zhang, X.-h.: Airborne laser radar measurement theory and method, pp. 43–44. Wuhan University Press, Wuhan (2007)
Chen, L.C., Teo, T.A., Hsieh, C.H., Rau, J.Y.: Reconstruction of Building Models with Curvilinear Boundaries from Laser Scanner and Aerial Imagery. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 24–33. Springer, Heidelberg (2006)
Caldeira, J., Azevedo, R., Silva, C.A., Sousa, J.M.C.: CP with ACO. In: Supply-Chain Management Using ACO and Beam-ACO Algorithms, pp. 1–6. IEEE Press, Los Alamitos (2008)
Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. J. European Journal of Operational Research (2006) (in press) (Corrected Proof)
Khichane, M., Albert, P., Solnon, C.: CP with ACO. In: Perron, L., Trick, M.A. (eds.) CPAIOR 2008. LNCS, vol. 5015, pp. 328–332. Springer, Heidelberg (2008)
Haibin, D.: Principle and application of ant colony algorithm, pp. 303–304. Science Press, Beijing (2005)
Ouadfel, S., Batouche, M.: Ant colony system to image texture classification. In: Proceedings of International Conference on Machine Learning and Cybernetics, pp. 1491–1495 (2003)
Leng, M., Yu, S.: An Effective Multi-level Algorithm Based on Ant Colony Optimization for Bisecting Graphs. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 138–149. Springer, Heidelberg (2007)
Zhuang, X.-H.: Image feature extraction with the perceptual graph based on the ant colony system. In: Proceedings of the IEEE International Conference on Systems Man, and Cybeinetics (SMC 2004), Washington, DC, pp. 6354–6359. IEEE Computer Society, Los Alamitos (2004)
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Wu, Y., Hu, Y., Lei, W., Zhao, N., Huang, T. (2010). Edge Detection of Laser Range Image Based on a Fast Adaptive Ant Colony Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_33
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DOI: https://doi.org/10.1007/978-3-642-13495-1_33
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