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dc.date.accessioned 2004-08-11T14:57:35Z
dc.date.available 2004-08-11T03:00:00Z
dc.date.issued 2004-04
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/9475
dc.description.abstract In this paper, an improvement of deformable template matching algorithm for polygonal objects in grayscale images using two-dimensional deformable templates along orthogonal curves is presented. In the process of pre-computing extensions of the deformable template along orthogonal curves, the novel matching approach incorporates adapting knowledge-specific template discretization techniques appropriate for different polygonal objects and minimizing the improved internal and external energy terms containing inter-shape information of polygonal objects. In our application, this energy optimization problem of the deformable template is efficiently solved by a genetic algorithm (GA). Our algorithm has been successfully applied on synthetic images and real images. The experiment results show that the new approach provides more robust and accurate matching method. en
dc.format.extent 20-25 es
dc.language en es
dc.subject genetic algorithm en
dc.subject deformable template; orthogonal curve; polygonal object; matching; genetic algorithm es
dc.subject orthogonal curve en
dc.title On improved deformable template matching for polygonal objects en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr04-3.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Luo, Jianshu es
sedici.creator.person Tang, Zeying es
sedici.creator.person Lu, Hanqing es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000389 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 4, no. 1 es


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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)