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Hardware Design Optimization for Human Motion Tracking Systems | A stochastic framework for evaluating and comparing the expected performance of sensing systems for interactive computer graphics
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639137256 ISBN 13: 9783639137255
Taschenbuch. Condition: Neu. Hardware Design Optimization for Human Motion Tracking Systems | A stochastic framework for evaluating and comparing the expected performance of sensing systems for interactive computer graphics | B. Danette Allen | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639137255 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Hardware Design Optimization for Human MotionTracking Systems
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639137256 ISBN 13: 9783639137255
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Allen B. DanetteDr. B. Danette Allen is a senior researcher at NASA LangleynResearch Center. She has extensive experience in the design andndevelopment of atmospheric science instruments and isninvestigating methods for modernizing t.
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Hardware Design Optimization for Human Motion Tracking Systems : A stochastic framework for evaluating and comparing the expected performance of sensing systems for interactive computer graphics
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639137256 ISBN 13: 9783639137255
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This research introduces a stochastic framework forevaluating and comparing the expected performance ofsensing systems for interactive computer graphics.Incorporating models of the sensor devices andexpected user motion dynamics, this framework enablescomplementary system- and measurement-level hardwareinformation optimization, independent of algorithmand motion paths. The approach for system-leveloptimization is to estimate the asymptotic positionand/or orientation uncertainty at many pointsthroughout a desired working volume or surface, andto visualize the results graphically. This globalperformance estimation can provide both a quantitative assessment of the expected performanceand intuition about how to improve the type andarrangement of sources and sensors, in the context ofthe desired working volume and expected scenedynamics. Using the same model components requiredfor these system-level optimization, the optimalsensor sampling time can be determined with respectto the expected scene dynamics for measurement-leveloptimization.
