Human Grasping Interaction Capture and Analysis

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dc.contributor.authorVerider, Benjaminen_US
dc.contributor.authorAndrews, Sheldonen_US
dc.contributor.authorKry, Paul G.en_US
dc.contributor.editorBernhard Thomaszewski and KangKang Yin and Rahul Narainen_US
dc.date.accessioned2017-12-31T10:43:01Z
dc.date.available2017-12-31T10:43:01Z
dc.date.issued2017
dc.description.abstractWe design a system to capture, clean, and segment a high quality database of hand based grasping and manipulation. We capture interactions with a large collection of everyday objects. Optical marker-based motion capture and glove data are combined in a physics-based filter to improve the quality of thumb motion. Sensors stitched into our glove provide recordings of the pressure image across the fingers and the palm. We evaluate di erent segmentation techniques for processing motion and pressure data. Finally, we describe examples that explain how the data will be useful in applications such as virtual reality and the design of physics-based control of virtual and robotic hands.en_US
dc.description.sectionheadersPoster Abstracts
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
dc.identifier.doi10.1145/3099564.3108163
dc.identifier.isbn978-1-4503-5091-4
dc.identifier.pagesBenjamin Verider, Sheldon Andrews, and Paul G. Kry-Computing methodologies Motion capture; Motion processing; hands, grasping, interaction capture, segmentation
dc.identifier.urihttps://doi.org/10.1145/3099564.3108163
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3099564-3108163
dc.publisherACMen_US
dc.subjectComputing methodologies Motion capture
dc.subjectMotion processing
dc.subjecthands
dc.subjectgrasping
dc.subjectinteraction capture
dc.subjectsegmentation
dc.titleHuman Grasping Interaction Capture and Analysisen_US
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SCA 17: Posters