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Human actions captured in video sequences are threedimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and RNNs). CNN based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. RNNs, especially Long Short- Term Memory (LSTM),...
Urrently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation...
The C2 (Command and Control, C2) behavior model has great influence on analytical simulation system. However the current models have some weaknesses as hard modeling, poor expansibility and little flexibility. In this paper, the behavior model architecture has been designed according to the analysis of the capabilities of the C2 behavior model. The capabilities are developed as standard and independent...
Behavior models play an important role in analytical simulation system. However, weaknesses such as hard modeling, poor expansibility and little flexibility still exist in current models. In this paper, a behavior model is designed, which consists of Rule-based Expert system and Mission Components. The expert system has high efficient and flexible auto-decision capability. Mission Component provides...
The deblocking filter in H.264/AVC is one of the most time consuming part of video decoder as its high content adaptation and data dependency lead to lots of computation. In this paper, we propose a novel parallel deblocking filter design based on the H.264/AVC video coding standard, taking the advantage that the data dependency of the deblocking filter are “periodic” in one dimension. Our proposed...
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