In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research results are presented and discussed to show the efficiency of this evolutionary computation method.

Simplified firefly algorithm for 2D image key-points search / Napoli, C; Pappalardo, G; Tramontana, E; Marszalek, Z; Polap, D; Wozniak, M. - (2014), pp. 1-8. (Intervento presentato al convegno 2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2014 tenutosi a Orlando; United States) [10.1109/CIHLI.2014.7013395].

Simplified firefly algorithm for 2D image key-points search

Napoli C
;
2014

Abstract

In order to identify an object, human eyes firstly search the field of view for points or areas which have particular properties. These properties are used to recognise an image or an object. Then this process could be taken as a model to develop computer algorithms for images identification. This paper proposes the idea of applying the simplified firefly algorithm to search for key-areas in 2D images. For a set of input test images the proposed version of firefly algorithm has been examined. Research results are presented and discussed to show the efficiency of this evolutionary computation method.
2014
2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2014
Image Processing; Computational Intelligence; Evolutionary Algorithms
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Simplified firefly algorithm for 2D image key-points search / Napoli, C; Pappalardo, G; Tramontana, E; Marszalek, Z; Polap, D; Wozniak, M. - (2014), pp. 1-8. (Intervento presentato al convegno 2014 IEEE Symposium on Computational Intelligence for Human-Like Intelligence, CIHLI 2014 tenutosi a Orlando; United States) [10.1109/CIHLI.2014.7013395].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1328744
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