The Global-Local transformation for noise resistant shape representation

Raftopoulos, KA; Kollias, SD

dc.contributor.author	Raftopoulos, KA	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T01:37:20Z
dc.date.available	2014-03-01T01:37:20Z
dc.date.issued	2011	en
dc.identifier.issn	1077-3142	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21496
dc.subject	Global-Local transformation	en
dc.subject	Shape recognition	en
dc.subject	Shape representation	en
dc.subject	Shape transformation	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Boundary curvature	en
dc.subject.other	Complexity reduction	en
dc.subject.other	Descriptors	en
dc.subject.other	Execution time	en
dc.subject.other	Expressive power	en
dc.subject.other	Global feature	en
dc.subject.other	Global-local	en
dc.subject.other	Integral invariant	en
dc.subject.other	Local feature	en
dc.subject.other	Low complexity	en
dc.subject.other	Matching methods	en
dc.subject.other	Object shape	en
dc.subject.other	Recognition abilities	en
dc.subject.other	Shape descriptors	en
dc.subject.other	Shape features	en
dc.subject.other	Shape recognition	en
dc.subject.other	Shape representation	en
dc.subject.other	Shape transformation	en
dc.subject.other	Smoothing kernels	en
dc.subject.other	State-of-the-art methods	en
dc.subject.other	Theoretical result	en
dc.subject.other	Useful properties	en
dc.subject.other	Curve fitting	en
dc.subject.other	Two dimensional	en
dc.subject.other	Value engineering	en
dc.title	The Global-Local transformation for noise resistant shape representation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cviu.2011.03.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cviu.2011.03.009	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The use of smoothing kernels in boundary curvature calculations, affects both object shape and the localization of edges. The Global-Local transformation (GLT), addresses this issue by providing a framework for shape representation, such that local and global features are simultaneously represented, even in noisy shapes, without the need for smoothing. By means of two-dimensional manifolds (surfaces), embedded into the unit cube, useful properties of the transform space are explored. The expressive power of the GLT is demonstrated by means of a global descriptor, called View Area Representation (VAR). VAR is an intuitive and physically meaningful shape descriptor which is robust to noise, captures curvature and leads to the introduction of novel and hybrid (global/local) shape features. A series of proofs is presented that link VAR and its derivatives to those shape features, providing the basis for shape representation involving global and local features in the presence of noise. The theoretical results are shown to be effective in matching noisy shapes by improving the recognition capability, of Local Area Integral Invariant (LAII), a relevant state of the art method of low complexity. A combination of GLT with VAR is used to define a new matching method certain advantages of which, in recognition ability and execution time, renders the intuitive properties of VAR significant for complexity reduction. (C) 2011 Elsevier Inc. All rights reserved.	en
heal.publisher	ACADEMIC PRESS INC ELSEVIER SCIENCE	en
heal.journalName	Computer Vision and Image Understanding	en
dc.identifier.doi	10.1016/j.cviu.2011.03.009	en
dc.identifier.isi	ISI:000292020900008	en
dc.identifier.volume	115	en
dc.identifier.issue	8	en
dc.identifier.spage	1170	en
dc.identifier.epage	1186	en