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Segmentation of an interesting target from a dynamic video has been an important research topic in computer vision. In this work, we present a novel recursive Bayesian learning method for dynamic video segmentation. In the algorithm, each frame pixel is represented as layered normal distributions and the recursive Bayesian estimation is used to update the background parameters so as to obtain a robust...
This work presents a novel Bayesian learning method for dynamic video segmentation. In the algorithm, each frame pixel is represented as layered normal distributions and the recursive Bayesian estimation is used to update the background parameters to obtain a robust background model. In the segmentation, foreground is separated by simple background subtraction method firstly. And then, a local texture...
Our paper presents a new approach for the recognition of highlights in soccer video. Our contribution consists of the combination of Bayesian theorem inferences and Hidden Markov Models (HMMs). We build HMMs to calculate probabilities that a test video segment belongs to highlight and non highlight classes. Then, we apply the Bayesian theorem on the two previous probabilities. Our system has achieved...
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