Affine-invariant modeling of shape-appearance images applied on sign language handshape classification

Roussos, A; Theodorakis, S; Pitsikalis, V; Maragos, P

dc.contributor.author	Roussos, A	en
dc.contributor.author	Theodorakis, S	en
dc.contributor.author	Pitsikalis, V	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:46:40Z
dc.date.available	2014-03-01T02:46:40Z
dc.date.issued	2010	en
dc.identifier.issn	15224880	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32771
dc.subject	Affine Transformation	en
dc.subject	Sign Language	en
dc.subject.other	Affine transformations	en
dc.subject.other	Hand configuration	en
dc.subject.other	Image coefficient	en
dc.subject.other	Linear combinations	en
dc.subject.other	Pose variation	en
dc.subject.other	Segmentation and tracking	en
dc.subject.other	Sign language	en
dc.subject.other	Video data	en
dc.subject.other	Image processing	en
dc.subject.other	Linear transformations	en
dc.subject.other	Imaging systems	en
dc.title	Affine-invariant modeling of shape-appearance images applied on sign language handshape classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2010.5651358	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2010.5651358	en
heal.identifier.secondary	5651358	en
heal.publicationDate	2010	en
heal.abstract	We propose a novel affine-invariant modeling of hand shapeappearance images, which offers a compact and descriptive representation of the hand configurations. Our approach combines: 1) A hybrid representation of both shape and appearance of the hand that models the handshapes without any landmark points. 2) Modeling of the shape-appearance images with a linear combination of variation images that is followed by an affine transformation, which accounts for modest pose variation. 3) Finally, an optimization based fitting process that results on the estimated variation image coefficients that are further employed as features. The proposed modeling is applied on handshapes from Sign Language video data after segmentation and tracking. It is evaluated on extensive experiments of handshape classification, which investigate the effect of the involved parameters and moreover provide a variety of comparisons to baseline approaches found in the literature. The results of at least 10.5% absolute improvement indicate the effectiveness of our approach in the handshape classification problem. © 2010 IEEE.	en
heal.journalName	Proceedings - International Conference on Image Processing, ICIP	en
dc.identifier.doi	10.1109/ICIP.2010.5651358	en
dc.identifier.spage	1417	en
dc.identifier.epage	1420	en