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In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation...
In this paper, we propose the use of Multi-entity Bayesian networks (MEBNs) for modeling the knowledge and analyzing the content pertaining to the domain of Intangible Cultural Heritage (ICH). MEBNs provide a rigorous knowledge representation framework in conjunction with reasoning and probabilistic inference capabilities. There are mainly two reasons motivating the use of MEBNs in the domain of ICH...
A general Bayesian post-processing methodology for performance improvement of object tracking in stereo video sequences is proposed in this paper. We utilize the results of any single channel visual object tracker in a Bayesian framework, in order to refine the tracking accuracy in both stereo video channels. In this framework, a variational Bayesian algorithm is employed, where prior knowledge about...
In this paper, we deal with object tracking in stereo video sequences. We introduce a Bayesian framework for utilizing the results of any conventional single channel object tracker, in order to accomplish the refinement of the tracking accuracy in the left/right video channel. In this Bayesian framework, a variational Bayesian algorithm is employed to this end, where a priori information about the...
In this paper we propose a Bayesian framework for accurate object tracking in stereoscopic sequences. Object detection and forward tracking are first combined according to predefined rules to get a first set of tracked regions candidates. Backward tracking is then applied to provide another set of possible object localizations. Moreover, this strategy is applied herein in stereoscopic video. We introduce...
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 difference. The proposed model takes into...
In this paper we propose a maximum a posteriori (MAP) framework for the super resolution problem, i.e. reconstructing high-resolution images from shifted, low-resolution degraded observations. In this framework the restoration, interpolation and registration subtasks of this problem are preformed simultaneously. The main novelties of this work are the use of a new hierarchical non-stationary edge...
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