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Probabilistic tracking algorithms typically using linear structure to update the learning model. Such linear structure is not appropriate for long-term robust tracking as the occlusion and other challenging factors may interfere the processing of incoming frames. Recently a spatio-temporal context (STC) algorithm based on Bayesian framework has using the context information between the target and...
Despite the recent success of extensive co-segmentation studies, they still suffer from limitations in accommodating multiple-foreground, large-scale, high-variability image set, as well as their underlying capability for parallel implementation. To improve, this paper proposes a bi-harmonic distance governed flexible method for the robust coherent segmentation of the overlapping/similar contents...
We study average consensus for directed graphs with quantized communication. In the presence of quantization errors, average consensus cannot be accurately reached. We develop robust consensus algorithms to reduce the effect of quantization. Linear matrix inequalities are used as design tools. Numerical results demonstrate the effectiveness of this method.
Collaborative spectrum sensing has been proposed recently to facilitate precise detection of Primary Users in Cognitive Radio networks. However, it simultaneously introduces new security issue that the selfish or even misbehaving users could cheat a secondary user by depriving its access opportunity. To address this problem, we propose a novel User-centric Misbehavior Detection Scheme (UMDS) in this...
In this paper, a newly vision-based localization method for mobile robots is presented. The cost of hardware for this localization system is very low. Based on background subtraction and optical flow tracking, the presented method overcomes the shortcoming of general background subtracting method- being sensitive to noises and interferences of other mobile objects. Besides, the presented method is...
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