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A temporal reasoning technique is presented for scene analysis. Rather than simply classifying everything in a scene as either dynamically moving foreground or long-lasting, stationary background, a temporal model is used to place each scene object in time relative to each other. It attributes each scene object with temporal information, such as object age and observability (i.e., how often it is...
In this paper, we present a new pixel-level scene model for segmenting video into foreground and background structure. The design of the model is largely influenced by several recently reported stochastic background models that have been shown to significantly outperform traditional deterministic techniques. In contrast to existing nonparametric scene models, we propose a learning algorithm that integrates...
In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new target system using existing data or knowledge from one or more previous source systems. By extending the theory of standard machine learning techniques, this framework allows us to solve many challenging problems directly and intuitively...
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