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. (C) 2009 Elsevier Ltd. All rights reserved.
|Autori:||Cerello P; Cheran SC; Bagnasco S; Bellotti R; Bolanos L; Catanzariti E; De Nunzio G; Fantacci M; Fiorina E; Gargano G; Gemme G; Torres EL; Masala GL; Peroni C; Santoro M|
|Titolo:||3-D object segmentation using ant colonies|
|Anno del prodotto:||2010|
|Digital Object Identifier (DOI):||10.1016/j.patcog.2009.10.007|
|Appare nelle tipologie:||1.1 Articolo in rivista|