Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73163
Título: Neural-like network model for color images analysis systems
Autores/as: Benitez-Diaz, D. 
Carrabina, J.
Gonzalez-Rodriguez, M.
Clasificación UNESCO: 220990 Tratamiento digital. Imágenes
120304 Inteligencia artificial
Fecha de publicación: 1994
Conferencia: 1994 IEEE International Conference on Neural Networks (ICNN 94) - 1st IEEE World Congress on Computational Intelligence 
Resumen: In this paper we present the Chromatic Neural-like Network. It is a two layer network architecture used in an image analysis system to learn objects classification tasks. Each processing unit in the hidden layer is considered as a network which codifies the color information at pixel level. The output layer makes a features analysis from the hidden layer responses in every window of the image. The architecture and operation of this network are extracted from the studies of biologic visual neural systems behavior and it is a model to be used in an artificial vision system. The learning methodology is an unsupervised, fuzzy and adaptive one and lets a faster training for image processing than other algorithms. It combines the Thresholding in Features Spaces technique and the Fuzzy Kohonen Clustering Nets approach with a gaussian membership function. Experimental results of the net performance with real images are shown.
URI: http://hdl.handle.net/10553/73163
ISBN: 0-7803-1901-X
Fuente: IEEE International Conference on Neural Networks - Conference Proceedings , v. 3, p. 1415-1420, (Diciembre 1994)
Colección:Actas de congresos
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