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In this paper we propose a robust sparse based visual tracking method by exploiting local representations in a particle filter framework. We construct a Multi-level Local Dictionary which consists of positive templates and negative templates for discriminative model, Which divide the positive and negative dictionary into two levels called static templates and dynamic templates, respectively, thus...
In this paper, we propose a novel collaborative appearance model for robust human tracking by exploiting both object and motion information in the bayesian framework. In contrast to most existing methods which use low or high-level visual cues, we use mid-level visual cues via superpixel with sufficient structure information to represent the object. In our work, the collaborative appearance is modeled...
This paper presents an image hierarchical super-resolution (SR) method with adaptive dictionaries, based on signal sparse representation. It can not only improve image detail quality but also reduce computational cost. Research on the human visual system suggests that our eyes are mainly sensitive to high-frequency contents. Inspired by this observation, we implemented a hierarchical process where...
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