Ant colony optimization inspired algorithm for 3D object segmentation

Authors:
Rafael Arnay;Leopoldo Acosta
Affiliations:
Department of System Engineering and Control and Computer Architecture, La Laguna University, La Laguna, Spain;Department of System Engineering and Control and Computer Architecture, La Laguna University, La Laguna, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

Citing 3
Cited 0

Ant Colony Optimization

Ant Colony Optimization
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Object categorization in clutter using additive features and hashing of part-graph descriptors

SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII

Quantified Score

Hi-index	0.00

Visualization

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.