In this work we describe the implementation of an artificial neural network, an extension of Hopfield's model, for the segmentation of textured images. We use a Markov random field in order to model the textures in the image. The problem is approached in terms of the minimization of a cost function that is projected onto the network. It provides a locally optimal solution to the problem of the classification of M* M pixels into K classes (textures). The experimental results obtained on artificial and natural images show the validity of the architecture we propose
© 2001-2024 Fundación Dialnet · Todos los derechos reservados