Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3222
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dc.contributor.authorPanigrahi, Amrutnarayan-
dc.contributor.authorMohanty, Jaganath Prasad-
dc.contributor.authorSwain, Ayaskanta-
dc.contributor.authorMahapatra, Kamalakanta-
dc.date.accessioned2019-01-28T04:47:27Z-
dc.date.available2019-01-28T04:47:27Z-
dc.date.issued2018-12-
dc.identifier.citationIEEE International Symposium on Smart Electronic Systems (IEEE-iSES 2018), Hydrabad, India, 17-19 December, 2018en_US
dc.identifier.urihttp://hdl.handle.net/2080/3222-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThe focus on Human-Computer Interaction (HCI) research is increasing day by day, due to the increasing requirement of intelligent input devices. Hand Gesture Recognition is a small sub-field but presents a significant number of applications and consumer products. Most researches target on the feasibility of recognition systems but give less weight to the device resources, so the cost and time. The time-consuming complicated algorithms' use is limited to special purpose devices such as expensive gaming consoles. The use of such systems in low cost embedded hardware in realtime circumstances is required, with the comfortability to use it. In this paper, we design an efficient real-time keyboard-like HCI using Static HGR. We have proposed and implemented new methods to reduce the time consumption while maintaining the high accuracy of 90% with scale and rotation invariance. Also, to maintain the comfort of use, we have eliminated complicated gestures and used only 11 gestures as input gesture set.en_US
dc.subjectGesture recognitionen_US
dc.subjectTop hat transformen_US
dc.subjectReference directionen_US
dc.subjectWrist identificationen_US
dc.subjectStatic HGRen_US
dc.subjectKinect sensoren_US
dc.titleReal-time efficient detection in Vision Based Static Hand Gesture Recognitionen_US
dc.typeArticleen_US
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