RT conference output
T1 ECAS-II: A Hybrid Algorithm for the Construction of  Multidimensional Image Segmenters
A1 Priego Torres, Blanca María
A1 Bellas, Francisco
A1 Duro, Richard J.
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Hyperspectral image segmentation
K1 Cellular automata
K1 Evolutionary algorithm
AB In this paper, we present a hybrid algorithm that combines evolutionary methods with cellular automata for the segmentation of multidimensional images, with a focus on hyperspectral images. The algorithm enables the automatic generation of cellular automata transition rules by utilizing a training set composed of specifically designed synthetic RGB images. This approach simplifies the process significantly, as it circumvents the need for adequately labeled hyperspectral images. Furthermore, by adjusting the parameters of the synthetic RGB images used in training, the algorithm can produce a variety of high-dimensional segmentations. The proposed method has been evaluated on both synthetic and real hyperspectral images, demonstrating highly competitive segmentation results compared to other techniques reported in the literature.
PB IEEE
SN 9781479919604
YR 2015
FD 2015
LK http://hdl.handle.net/10498/34700
UL http://hdl.handle.net/10498/34700
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026