Adaptive classification of textured images using moments and autoregressive models

Sukissian, Levon; Tirakis, Andreas; Kollias, Stefanos

dc.contributor.author	Sukissian, Levon	en
dc.contributor.author	Tirakis, Andreas	en
dc.contributor.author	Kollias, Stefanos	en
dc.date.accessioned	2014-03-01T02:47:54Z
dc.date.available	2014-03-01T02:47:54Z
dc.date.issued	1990	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33429
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-17944389833&partnerID=40&md5=93e97fb8f8f6a0ad88e50f50fae84f3d	en
dc.subject.other	Computer Vision - Simulation	en
dc.subject.other	Mathematical Techniques - Least Squares Approximations	en
dc.subject.other	Neural Networks - Mathematical Models	en
dc.subject.other	Robotics - Vision Systems	en
dc.subject.other	Adaptive Classification	en
dc.subject.other	Moments of Images	en
dc.subject.other	Image Processing	en
dc.title	Adaptive classification of textured images using moments and autoregressive models	en
heal.type	conferenceItem	en
heal.publicationDate	1990	en
heal.abstract	An adaptive approach to the classification of textured images is presented, based on the extraction of appropriate features from images. Autoregressive linear prediction models, as well as moments of images, are features which are examined and compared in the paper. Classification is achieved in an adaptive way, using an artificial feedforward neural network, which is trained by examples, using an efficient variant of the backpropagation learning algorithm. It is also shown that an adaptive least squares estimation algorithm can be appropriately interweaved with the network, resulting in an on-line adaptive classification scheme. Simulation results are given, which illustrate the performance of the presented method.	en
heal.publisher	Publ by Int Soc for Optical Engineering, Bellingham, WA, United States	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.volume	1360 pt 3	en
dc.identifier.spage	1296	en
dc.identifier.epage	1306	en