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Real Time Dynamic System Stochastic Identification in Video Capture for Data Compression, Image Interpolation, Prediction, and Augmented Reality

Abstract : In computer vision, cameras more and more accurate, fast, 3D featured are used. These still evolutions generate more data, which is an issue for users to store it with standard compression for example for recording proof in case of products manufacture defective. The aim of this work is to develop a specific solution adapted for vision systems which have a known scenario and can be described by dynamic models. In this framework, Kalman filters are used for data compression, observable variable prediction, and augmented reality. The developed concepts are tested with a scenario of a ruler on a table. The experiment aims to check the data compression level, the estimation of the friction forces coefficient of the ruler and the prediction of the stop position.
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https://hal-univ-tln.archives-ouvertes.fr/hal-01723798
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Submitted on : Monday, March 5, 2018 - 5:50:32 PM
Last modification on : Wednesday, July 21, 2021 - 2:16:03 PM

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Adrien Goeller, Jean-Luc Dion, Thierry Soriano, Bernard Roux. Real Time Dynamic System Stochastic Identification in Video Capture for Data Compression, Image Interpolation, Prediction, and Augmented Reality. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Aug 2015, Boston, United States. pp.V008T13A054--V008T13A054, ⟨10.1115/DETC2015-47398⟩. ⟨hal-01723798⟩

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