The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adaptive Image Segmentation With Distributed Behavior-Based Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Image Thresholding Using Ant Colony Optimization
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A swarm intelligence approach to counting stacked symmetric objects
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Distributed medical images analysis on a Grid infrastructure
Future Generation Computer Systems
Hierarchical image segmentation using ant colony and chemical computing approach
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Varying the population size of artificial foraging swarms on time varying landscapes
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Swarm intelligence for medical volume segmentation: the contribution of self-reproduction
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A stochastic gravitational approach to feature based color image segmentation
Engineering Applications of Artificial Intelligence
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3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models. A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed. Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background. The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies.