This paper presents a novel algorithm for synthesizing animations of running water,such as water-falls and rivers,in the style of Chinese paintings,for applications such as cartoon making. All video frames are first registered in a common coordinate system,simultaneously segmenting the water from background and computing optical flow of the water. Taking artists’ advice into account,we produce a painting structure to guide painting of brush strokes. Flow lines are placed in the water following an analysis of variance of optical flow,to cause strokes to be drawn where the water is flowing smoothly,rather than in turbulent areas: this allows a few moving strokes to depict the trends of the water flows. A variety of brush strokes is then drawn using a template determined from real Chinese paintings. The novel contributions of this paper are:a method for painting structure generation for flows in videos,and a method for stroke placement,with the necessary temporal coherence.
ZHANG SongHaiCHEN TaoZHANG YiFeiHU ShiMinMARTIN Ralph
Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.
HE HaiTaoLUO XiaoNanMA FeiTengCHE ChunHuiWANG JianMin
