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.

Autore Pugliese

• De Nunzio Giorgio

Tutti gli autori

• P. Cerello , S.C. Cheran , S. Bagnasco , R. Bellotti , L. Bolanos , E. Catanzariti , G. De Nunzio , M.E. Fantacci , E. Fiorina , G. Gargano , G. Gemme , E. Lòpez Torres , G.L. Masala , C. Peroni , M. Santoro

Titolo volume/Rivista

PATTERN RECOGNITION

Anno di pubblicazione

2010

ISSN

0031-3203

ISBN

Non Disponibile

Numero di citazioni Wos

21

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

25

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile