Abstract
In this work, an ACO-based approach to the problem of 3D object segmentation is presented. Ant Colony Optimization (ACO) metaheuristic uses a set of agents to explore a search space, gathering local information and utilizing their common memory to obtain global solutions. In our approach to the 3D segmentation problem, the artificial ants start their exploratory movements in the outer contour of the object. They explore the surface of the object influenced by its curvature and by the trails followed by other agents. After a number of generations, particular solutions of the agents converge to the best global paths, which are used as borders to segment the object’s parts. This convergence mechanism avoids over-segmentation, detecting regions based on the global structure of the object and not on local information only.
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Dorigo, M., St\(\ddot{u}\)tzle, T.: The Ant Colony Optimization Metaheuristic: Algorithms, Applications and Advances. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. Springer, New York (2002)
Dorigo, M., St\(\ddot{u}\)tzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Dorigo, M., Maniezo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. on Systems, Man and Cybernetics Part B 26, 29–41 (1996)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. In: Proc. of International Conference on Robotics and Automation, ICRA (2011)
Marton, Z.-C., Balint-Benczedi, F., Seidel, F., Goron, L.C., Beetz, M.: Object Categorization in Clutter using Additive Features and Hashing of Part-graph Descriptors. In: Stachniss, C., Schill, K., Uttal, D. (eds.) Spatial Cognition 2012. LNCS (LNAI), vol. 7463, pp. 17–33. Springer, Heidelberg (2012)
Rabbani, T., Vosselman, G.: Segmentation of point clouds using smoothness constraint. In: ISPRS Commission V Symposium ‘Image Engineering and Vision Metrology’ (2006)
Pauling, F., Bosse, M., Zlot, R.: Automatic Segmentation of 3D Laser Point Clouds by Ellipsoidal Region Growing. In: Proc. of the Australasian Conference on Robotics & Automation (ACRA) (2009)
Golovinskiy, A., Funkhouser, T.: Min-cut based segmentation of point clouds. Princeton University
Moosmann, F., Pink, O., Stiller, C.: Segmentation of 3D Lidar Data in non-flat Urban Environments using a Local Convexity Criterion. In: IEEE Intelligent Vehicles Symposium, pp. 215–220 (2009)
Strom, J., Richardson, A., Olson, E.: Graph-based segmentation of colored 3d laser point clouds. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)
Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., Frenkel, A.: On the Segmentation of 3D LIDAR Point Clouds. In: International Conference on Robotics and Automation (ICRA) (2011)
Triebel, R., Shin, J.: Siegwart. R.: Segmentation and unsupervised part-based discovery of repetitive objects. In: Proceedings of Robotics: Science and Systems (2010)
Marton, Z.C., Pangercic, D., Blodow, N., Kleinehellefort, J., Beetz, J.: General 3D Modelling of Novel Objects from a Single View. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)
Rusu, R.B., Cousin, S.: 3D is here: Point Cloud Library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA) (2011)
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Arnay, R., Acosta, L. (2013). Ant Colony Optimization Inspired Algorithm for 3D Object Segmentation. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_25
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DOI: https://doi.org/10.1007/978-3-642-38679-4_25
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