Theodosios Gkamas

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The Horn-Schunck (HS) optical flow method is widely employed to initialize many motion estimation algorithms. In this work, a variational Bayesian approach of the HS method is presented where the motion vectors are considered to be spatially varying Student's t-distributed unobserved random variables and the only observation available is the temporal image(More)
The Horn-Schunck (HS) optical flow method is widely employed to initialize many motion estimation algorithms. In this work, a variational Bayesian approach of the HS method is presented, where the motion vectors are considered to be spatially varying Student’s t-distributed unobserved random variables, i.e., the prior is a multivariate Student’s(More)
In this paper, we show how the segmentation of an image into superpixels may be used as preprocessing paradigm to improve the accuracy of the optical flow estimation in an image sequence. Superpixels play the role of accurate support masks for the integration of the optical flow equation. We employ a variation of a recently proposed optical flow algorithm(More)
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