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Mostrar registro sencillo 2012-04-09T16:21:30Z 2012-04-09T03:00:00Z 2012-04
dc.description.abstract Vision based applications are present anywhere. A special market is industry, allowing to improve product quality and to reduce manufacturing costs. The vision systems applied to industries are known as machine vision systems. These systems must meet time constraints to operate in real time. Generally the production lines are more and more fasters, and the time to process and bring a response is minimal. For this reasons, dedicated architectures are emplaced. In this work a review of several commercial systems is presented, as well a proposed architecture is depicted. The architecture is concern as a customizable platform, avoiding having knowledge in hardware description languages. It is based on massive parallelism to achieve the maximum processing performance. Several optimizations at different levels are applied to increase the final system speedup. Also, time and area metrics are reported, showing that the architecture is well suitable for real time video processing in industrial applications. en
dc.format.extent p. 1-8 es
dc.language en es
dc.title High Performance Customizable Architecture for Machine Vision Applications en
dc.type Articulo es
sedici.identifier.uri es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Leiva, Lucas es
sedici.creator.person Acosta, Nelson es
sedici.subject.materias Informática es
sedici.subject.other video processing en
sedici.subject.other machine vision en
sedici.subject.other FPGA en
sedici.description.fulltext true es Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000008055 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 12, 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)