Cerello, Piergiorgio and Cheran, Sorin Cristian and Bagnasco, Stefano and Bellotti, Roberto and Bolanos, Lourdes and Catanzariti, Ezio and De Nunzio, Giorgio and Fantacci, Maria Evelina and Fiorina, Elisa and Gargano, Gianfranco and Gemme, Gianluca and López Torres, Ernesto and Masala, Giovanni Luca Christian and Peroni, Cristiana and Santoro, Matteo (2010) 3-D object segmentation using ant colonies. Pattern Recognition, Vol. 43 (4), p. 1476-1490. ISSN 0031-3203. Article.
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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.
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