FAST TECHNIQUE FOR AUTOMATIC SEGMENTATION AND CLASSIFICATION OF TEXTURED IMAGES.

Boutalis, Yiannis; Kollias, Stefanos; Carayannis, George; Sukissian, Levon

dc.contributor.author	Boutalis, Yiannis	en
dc.contributor.author	Kollias, Stefanos	en
dc.contributor.author	Carayannis, George	en
dc.contributor.author	Sukissian, Levon	en
dc.date.accessioned	2014-03-01T02:40:51Z
dc.date.available	2014-03-01T02:40:51Z
dc.date.issued	1988	en
dc.identifier.issn	07367791	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30253
dc.subject	a priori information	en
dc.subject	Automatic Segmentation	en
dc.subject	Computational Complexity	en
dc.subject	Distance Measure	en
dc.subject	Image Modeling	en
dc.subject	Parameter Estimation	en
dc.subject.other	SIGNAL FILTERING AND PREDICTION	en
dc.subject.other	STATISTICAL METHODS	en
dc.subject.other	AR IMAGE MODEL	en
dc.subject.other	AUTOREGRESSIVE IMAGE MODEL PARAMETER ESTIMATION	en
dc.subject.other	STATISTICAL DISTANCE MEASURE	en
dc.subject.other	TEXTURED IMAGES SEGMENTATION	en
dc.subject.other	IMAGE PROCESSING	en
dc.title	FAST TECHNIQUE FOR AUTOMATIC SEGMENTATION AND CLASSIFICATION OF TEXTURED IMAGES.	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.1988.196796	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.1988.196796	en
heal.publicationDate	1988	en
heal.abstract	A fast, computationally efficient method for automatic segmentation and classification of textured images is presented. The method does not necessarily need a-priori information about the textures present in the image, thus avoiding the necessity of a training set of textures. A fast adaptive multichannel technique for autoregressive image model parameter estimation with fast tracking capabilities and a powerful statistical distance measure are appropriately interweaved to form the proposed technique. Specific properties of the estimation part of the algorithm are exploited to reduce greatly the computational complexity of the distance measure. Some interesting extensions of the method are discussed and examples are given which illustrate the performance of the algorithm.	en
heal.publisher	IEEE, New York, NY, USA	en
heal.journalName	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en
dc.identifier.doi	10.1109/ICASSP.1988.196796	en
dc.identifier.spage	1132	en
dc.identifier.epage	1135	en