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Experimental film and video are represented by Coleen Fitzgibbon's "Daily News" and "L.E.S".
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In this way, each video was represented by the spatio-temporal information capturing BoW vector.
Each video is represented by four frame grabs — tapping on one will cause the video player to pop open the clip will start playing immediately.
The base layer of a scalable video is represented with a set of NALUs that can be decoded by any compatible AVC decoder.
Weak poses are predicted from shape context descriptors using trained Gaussian processes and the video is represented as a histogram of the vocabulary learned in the training phase.
Moreover, to further capture the spatio-temporal information between features, each video is represented with the algorithm detailed in section 3.3.
We denote the pose representation in the reduced dimensionality space as weak poses or ψ ′, and the motion sequence of UaSpace the i-th video is represented as: Ψ i = [ ψ 1 i ′, ψ 2 i ′, …, ψ n i i ′ ], (3).
In the data registration phase, each gallery video is represented by two feature matrices through the face and gesture representation modules, and it is added to user database in the form of two feature matrices.
The targets tracked in the videos are represented in the visualization by representative proxy models, both because the synchronization and resolution of the acquisition devices prohibit stereo reconstruction, and because unique and detailed geometry for every actor would constitute too much data to be efficiently managed in our distributed system.
The videos are represented by probability distributions and then meaningfully compared in a probabilistic framework.
As most compressed videos are represented to human users, perception-based end-to-end distortion model should be developed for error-resilient video coding.
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