Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection

Karantzalos, K; Paragios, N

dc.contributor.author	Karantzalos, K	en
dc.contributor.author	Paragios, N	en
dc.date.accessioned	2014-03-01T01:58:05Z
dc.date.available	2014-03-01T01:58:05Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28630
dc.subject	Building Detection	en
dc.subject	Building Extraction	en
dc.subject	Image Segmentation	en
dc.subject	Level Set	en
dc.subject	Object Detection	en
dc.subject	Optical Imaging	en
dc.subject	Quantitative Evaluation	en
dc.subject	Remote Sensing	en
dc.subject	Remote Sensing Data	en
dc.subject	Satellite Image	en
dc.subject	Shape Priors	en
dc.subject	Variational Method	en
dc.title	Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TGRS.2008.2002027	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TGRS.2008.2002027	en
heal.publicationDate	2009	en
heal.abstract	In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous approaches toward the integration of	en
heal.journalName	IEEE Transactions on Geoscience and Remote Sensing	en
dc.identifier.doi	10.1109/TGRS.2008.2002027	en