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dc.date.accessioned 2019-12-27T15:40:50Z
dc.date.available 2019-12-27T15:40:50Z
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/87940
dc.description.abstract This work presents a visual odometric system for camera tracking in underwater scenarios of the seafloor which are strongly perturbed with sunlight caustics and cloudy water. Particularly, we focuse on the performance and robustnes of the system, which structurally associates a deflickering filter with a visual tracker. Two state-of-the-art trackers are employed for our study, one pixel-oriented and the other feature-based. The contrivances of the trackers were crumbled and their suitability for underwater environments analyzed comparatively. To this end real subaquatic footages in perturbed environments were employed. en
dc.format.extent 151-164 es
dc.language en es
dc.subject Visual odometric system es
dc.subject Deflickering filter es
dc.subject Visual tracker es
dc.title A Robust Approach for Monocular Visual Odometry in Underwater Environments en
dc.type Objeto de conferencia es
sedici.identifier.issn 2451-7585 es
sedici.creator.person Jordán, Mario A. es
sedici.creator.person Trabes, Emanuel es
sedici.creator.person Delrieux, Claudio es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2019-09
sedici.relation.event XX Simposio Argentino de Inteligencia Artificial (ASAI 2019) - JAIIO 48 (Salta) es
sedici.description.peerReview peer-review es


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