3-D object segmentation using ant colonies

Cerello, P., Christian Cheran, S., Bagnasco, S., Bellotti, R., Bolanos, L., Catanzariti, E., De Nunzio, G., Evelina Fantacci, M., Fiorina, E., Gargano, G., and others. (2010) 3-D object segmentation using ant colonies. Pattern Recognition, 43 (4). pp. 1476-1490. ISSN 0031-3203. (doi:10.1016/j.patcog.2009.10.007) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91428)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: https://doi.org/10.1016/j.patcog.2009.10.007

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.

Item Type:	Article
DOI/Identification number:	10.1016/j.patcog.2009.10.007
Additional information:	cited By 30
Uncontrolled keywords:	Artificial life; Ant colony; Image processing; 3-D object segmentation
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Amy Boaler
Date Deposited:	08 Nov 2021 14:52 UTC
Last Modified:	05 Nov 2024 12:57 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/91428 (The current URI for this page, for reference purposes)

University of Kent Author Information

Masala, Giovanni Luca.

Creator's ORCID:	https://orcid.org/0000-0001-6734-9424
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