Adaptive segmentation of textured images using linear prediction and neural networks

Kollias, S; Sukissian, L

dc.contributor.author	Kollias, S	en
dc.contributor.author	Sukissian, L	en
dc.date.accessioned	2014-03-01T02:48:06Z
dc.date.available	2014-03-01T02:48:06Z
dc.date.issued	1992	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33522
dc.subject	Least Square	en
dc.subject	Linear Predictive	en
dc.subject	Network Architecture	en
dc.subject	Prior Information	en
dc.subject	Recursive Estimation	en
dc.subject	Neural Network	en
dc.title	Adaptive segmentation of textured images using linear prediction and neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NNSP.1992.253672	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NNSP.1992.253672	en
heal.publicationDate	1992	en
heal.abstract	An adaptive technique for classifying and segmenting textured images is presented. This technique uses an efficient least squares algorithm for recursive estimation of two-dimensional autoregressive texture models and neural networks for recursive classification of the models. A network with fixed, but space-varying, interconnection weights is used to optimally select a small representative set of these models, while a network with	en
heal.journalName	IEEE XIII Workshop on Neural Networks for Signal Processing	en
dc.identifier.doi	10.1109/NNSP.1992.253672	en