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Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
This paper presents a robust head tracking algorithm based on multiple cues fusion in a kernel-Bayesian framework. In this algorithm, the object to be tracked is characterized using a spatial-constraint mixture of the Gaussians-based appearance model and a multichannel chamfer matching-based shape model. These two models complement each other and their combination is discriminative in distinguishing...
This paper proposes a simple and a robust method to detect and extract the silhouettes from a video sequence of a static camera based on background subtraction technique. The proposed method analyse the pixel history as a time series observations. A robust technique to detect motion based on kernel density estimation is presented. Two consecutive stages of the k-means clustering algorithm are utilized...
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