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.

3-D object segmentation using ant colonies

BOLANOS, LOURDES;FIORINA, ELISA;PERONI, Cristiana;
2010

Abstract

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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Piergiorgio Cerello; Sorin Christian Cheran; Stefano Bagnasco; Roberto Bellotti; Lourdes Bolanos; Ezio Catanzariti; Giorgio De Nunzio; Maria Evelina Fantacci; Elisa Fiorina; Gianfranco Gargano; Gianluca Gemme; Ernesto López Torres; Gian Luca Masala; Cristiana Peroni; Matteo Santoro
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/67364
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