Affine-invariant curve normalization for object shape representation, classification, and retrieval

Avrithis, Y; Xirouhakis, Y; Kollias, S

dc.contributor.author	Avrithis, Y	en
dc.contributor.author	Xirouhakis, Y	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:16:07Z
dc.date.available	2014-03-01T01:16:07Z
dc.date.issued	2001	en
dc.identifier.issn	0932-8092	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13922
dc.subject	Affine invariants	en
dc.subject	Curve normalization	en
dc.subject	Image and video retrieval	en
dc.subject	Shape analysis	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Cybernetics	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Discrete Fourier transforms	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image retrieval	en
dc.subject.other	Object recognition	en
dc.subject.other	Curve normalization	en
dc.subject.other	Multimedia systems	en
dc.title	Affine-invariant curve normalization for object shape representation, classification, and retrieval	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/PL00013272	en
heal.identifier.secondary	http://dx.doi.org/10.1007/PL00013272	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	A novel method for two-dimensional curve normalization with respect to affine transformations is presented in this paper, which allows an affine-invariant curve representation to be obtained without any actual loss of information on the original curve. It can be applied as a preprocessing step to any shape representation, classification, recognition, or retrieval technique, since it effectively decouples the problem of affine-invariant description from feature extraction and pattern matching. Curves estimated from object contours are first modeled by cubic B-splines and then normalized in several steps in order to eliminate translation, scaling, skew, starting point, rotation, and reflection transformations, based on a combination of curve features including moments and Fourier descriptors.	en
heal.publisher	SPRINGER-VERLAG	en
heal.journalName	Machine Vision and Applications	en
dc.identifier.doi	10.1007/PL00013272	en
dc.identifier.isi	ISI:000172501100004	en
dc.identifier.volume	13	en
dc.identifier.issue	2	en
dc.identifier.spage	80	en
dc.identifier.epage	94	en