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With the development of information technology, the automatic recognition of human action from video becomes a very popular research topic. In this paper, we review recent state-of-the-art of human action recognition methods in videos. First, we compare several notable handcrafted methods. Then we introduce some deep learning action recognition models. As deep learning becomes hot spot of research...
Paper describe an NP-video phenomena model system. It provides a simple non-photorealistic scene model in which users can not only obtain an abstraction, infinite video but also obtain enjoyable and real-time artistic experience. Firstly, we design a new algorithm for fast converting a photo or image to a synthesized painting of abstraction. Secondly, we propose a new modeling video texture based...
This paper proposed an improved video-based texture synthesis approach for simulating realistic natural phenomena. The algorithm measures similarity of every two frames to determine a preliminary playing sequence and then divide the preliminary playing sequence into several subsequences with different frame-to-frame delay so as to remove visual discontinuities. For removing the abrupt change from...
In this paper, we present a GPU based video stylization framework that can artistically stylize video stream in real time. In this framework, firstly, we use a separable implementation of bilateral filter as an adaptive and iterative smoothing operation that selectively simplifies image color, leading to an abstracted look. Secondly, we perform a soft color quantization step on the abstracted video...
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