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Tangible UI by object and material classification with radar

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Yeo_2017_Tangible_UI_SA17_AAM.pdf (14.92Mb)
Date
27/11/2017
Author
Yeo, Hui Shyong
Ens, Barrett
Quigley, Aaron John
Keywords
Radar sensing
Tangible interaction
Object recognition
QA75 Electronic computers. Computer science
T Technology
NS
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Abstract
Radar signals penetrate, scatter, absorb and reflect energy into proximate objects and ground penetrating and aerial radar systems are well established. We describe a highly accurate system based on a combination of a monostatic radar (Google Soli), supervised machine learning to support object and material classification based Uls. Based on RadarCat techniques, we explore the development of tangible user interfaces without modification of the objects or complex infrastructures. This affords new forms of interaction with digital devices, proximate objects and micro-gestures.
Citation
Yeo , H S , Ens , B & Quigley , A J 2017 , Tangible UI by object and material classification with radar . in SA '17 SIGGRAPH Asia 2017 Emerging Technologies . , 14 , ACM , New York , 10th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia , Bangkok , Thailand , 27/11/17 . https://doi.org/10.1145/3132818.3132824
 
conference
 
Publication
SA '17 SIGGRAPH Asia 2017 Emerging Technologies
DOI
https://doi.org/10.1145/3132818.3132824
Type
Conference item
Rights
© 2017, the Owner(s) / the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3132818.3132824
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/12965

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