Modulation-feature based textured image segmentation using curve evolution

Kokkinos, I; Evangelopoulos, G; Maragos, P

dc.contributor.author	Kokkinos, I	en
dc.contributor.author	Evangelopoulos, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:42:53Z
dc.date.available	2014-03-01T02:42:53Z
dc.date.issued	2004	en
dc.identifier.issn	15224880	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31122
dc.subject	Component Analysis	en
dc.subject	Curve Evolution	en
dc.subject	Feature Vector	en
dc.subject	Image Segmentation	en
dc.subject	Texture Analysis	en
dc.subject	Texture Segmentation	en
dc.subject	Variational Models	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer vision	en
dc.subject.other	Image analysis	en
dc.subject.other	Integral equations	en
dc.subject.other	Lagrange multipliers	en
dc.subject.other	Mathematical models	en
dc.subject.other	Modulation	en
dc.subject.other	Textures	en
dc.subject.other	Dominant components analysis (DCA)	en
dc.subject.other	Nonlinear algorithms	en
dc.subject.other	Segmentation algorithms	en
dc.subject.other	Texture segmentation	en
dc.subject.other	Image segmentation	en
dc.title	Modulation-feature based textured image segmentation using curve evolution	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2004.1419520	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2004.1419520	en
heal.publicationDate	2004	en
heal.abstract	In this paper we incorporate recent results from AM-FM models for texture analysis into the variational model of image segmentation and examine the potential benefits of using the combination of these two approaches for texture segmentation. Using the Dominant Components Analysis (DCA) technique we obtain a low-dimensional, yet rich texture feature vector that proves to be useful for texture segmentation. We use an unsupervised scheme for texture segmentation, where only the number of regions is known a-priori. Experimental results on both synthetic and challenging real-world images demonstrate the potential of the proposed combination. © 2004 IEEE.	en
heal.journalName	Proceedings - International Conference on Image Processing, ICIP	en
dc.identifier.doi	10.1109/ICIP.2004.1419520	en
dc.identifier.volume	5	en
dc.identifier.spage	1201	en
dc.identifier.epage	1204	en