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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Dias, J., Pedro Nande, Nuno Barata & Joao Correia (2004). O.G.R.E. - Open Gestures Recognition Engine. In Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing. (pp. 33-40). Curitiba: IEEE.
Exportar Referência (IEEE)
J. M. Dias et al.,  "O.G.R.E. - Open Gestures Recognition Engine", in Proc.. 17th Brazilian Symp. on Computer Graphics and Image Processing, Curitiba, IEEE, 2004, pp. 33-40
Exportar BibTeX
	author = "Dias, J. and Pedro Nande and Nuno Barata and Joao Correia",
	title = "O.G.R.E. - Open Gestures Recognition Engine",
	booktitle = "Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing",
	year = "2004",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/SIBGRA.2004.1352940",
	pages = "33-40",
	publisher = "IEEE",
	address = "Curitiba",
	organization = "",
	url = "https://ieeexplore.ieee.org/document/1352940"
Exportar RIS
TI  - O.G.R.E. - Open Gestures Recognition Engine
T2  - Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing
AU  - Dias, J.
AU  - Pedro Nande
AU  - Nuno Barata
AU  - Joao Correia
PY  - 2004
SP  - 33-40
SN  - 1530-1834
DO  - 10.1109/SIBGRA.2004.1352940
CY  - Curitiba
UR  - https://ieeexplore.ieee.org/document/1352940
AB  - In this paper, we describe a hand gesture recognition engine based on computer vision (CV), as a computing platform to support gesture interaction between humans and computers. Presenting a simple approach to recognizing gestures through image processing techniques and a single video camera, we address the problem of generic hand gestures recognition, especially of spelled sign language hand poses, introducing a preliminary study to its kinetic component. In our methodology, the system initially removes the background of captured images, eliminating irrelevant pixel information. The human hand is then detected, segmented and its contours localized. From these contours significant metrics are derived, allowing a search in a pre-defined hand poses' library, where each pose is previously converted into a set of metric values. We discuss several algorithmic options to support our methodology and present experimental results, regarding the recognition of Portuguese sign language signs. We discuss future directions of our work.
ER  -