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dc.contributor.authorLee, Younghoen_US
dc.contributor.authorPiumsomboon, Thammathipen_US
dc.contributor.authorEns, Barretten_US
dc.contributor.authorLee, Gun A.en_US
dc.contributor.authorDey, Arindamen_US
dc.contributor.authorBillinghurst, Marken_US
dc.contributor.editorTony Huang and Arindam Deyen_US
dc.date.accessioned2017-11-21T15:42:08Z
dc.date.available2017-11-21T15:42:08Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-052-9
dc.identifier.urihttp://dx.doi.org/10.2312/egve.20171364
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egve20171364
dc.description.abstractThe rapid development of machine learning algorithms can be leveraged for potential software solutions in many domains including techniques for depth estimation of human eye gaze. In this paper, we propose an implicit and continuous data acquisition method for 3D gaze depth estimation for an optical see-Through head mounted display (OST-HMD) equipped with an eye tracker. Our method constantly monitoring and generating user gaze data for training our machine learning algorithm. The gaze data acquired through the eye-tracker include the inter-pupillary distance (IPD) and the gaze distance to the real and virtual target for each eye.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectMixed / augmented reality
dc.titleA Gaze-depth Estimation Technique with an Implicit and Continuous Data Acquisition for OST-HMDsen_US
dc.description.seriesinformationICAT-EGVE 2017 - Posters and Demos
dc.description.sectionheadersPosters A
dc.identifier.doi10.2312/egve.20171364
dc.identifier.pages1-2


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