Weina Fu

Distribution of primary additional errors in fractal encoding method

Today, fractal image encoding method becomes an effective loss compression method in multimedia without resolution, and its negativeness is that its high computational complexity. So many approximate methods are given to decrease the computation time. So the distribution of error points is valued to research. In this paper, by extracted primary additional error values, we first present a novel fast fractal encoding method. Then, with the extracted primary additional error values, we abstract the distribution of these values. We find that the different distribution of values denotes the different parts in images. Finally, we analyze the experimental results and find some properties of these values. The experimental results also show the effectiveness of the method.

A review of visual moving target tracking

Recently, computer vision and multimedia understanding become important research domains in computer science. Meanwhile, visual tracking of moving target, one of most important application in computer vision, becomes a highlight today. So, this paper reviews research and technology in this domain. First, background and application of visual tracking is introduced. Then, visual tracking methods are classified by different thinking and technologies. Their positiveness, negativeness and improvement are analyzed deeply. Finally, difficulty in this domain is summarized and future prospect of related fields is presented.

A Novel Fusion Method by Static and Moving Facial Capture

For many years, face recognition has been one of the most important domains in pattern recognition. Nowadays, face recognition is more required to be used in video actually. So moving facial capture must be studied firstly because of performance requirement. Since classic facial capture method is not so suitable in a moving environment, in this paper, we present a novel facial capture method in a moving environment. Firstly, continuous frames are extracted from detecting videos by similar characteristics. Then, we present an algorithm to extract the moving object and restructure background. Meanwhile, with analysis of skin color in both moving and static areas, we use the classic faces capture method to catch all faces. Finally, experimental results show that this method has better robustness and accuracy.

Distributional Fractal Creating Algorithm in Parallel Environment

Nowadays, the fractal is used widely everywhere. Then, its creating time becomes an important study area for complex iteration functions because the escape-time algorithm (ETA), which is the most used algorithm in fractal creating, performs not so well in this condition. In this paper, in order to solve this problem, we improve ETA into the parallel environment and reach well performance. At first, we provide a separation method of ETA to reform it into a SIMC-MC2 grid. Secondly, we prove its correctness and compute the complexity of this novel parallel algorithm. Meantime, we separate an improved ETA which we have presented into the same parallel environment and compute its complexity. Additionally, theoretical and experimental results show the characteristics of this novel algorithm. Finally, the computational result shows that a novel environment is needed to decrease large manual allocation strategies, which block the improved benefit.

Differential trajectory tracking with automatic learning of background reconstruction

Nowadays, trajectory tracking technology is widely used in many outdoor applications, such as intelligent traffic and video surveillance. However, most of trajectory-tracking technologies rely on a static background, which is hard to obtain in many situations. Obviously, these methods are out of action in the case of dynamic background. In this paper, a novel trajectory tracking method is presented, which is implemented with a new background reconstruction algorithm. Firstly, the background is assumed to be a blank scene. Then, the background is reconstructed by means of video detection that places moving objects in the scene. Finally, real-time trajectories of moving objects are computed based on the reconstructed background. Experimental results show its robustness and practicability even in a cluttered background.

Moving tracking with approximate topological isomorphism

Today, tracking of moving objects in video becomes a highlight in multimedia. This paper proposes a novel method, which is suitable for applying on relatively high-resolution videos that moving objects can be distinguished from their color and shape information. This method matches and tracks multiple moving objects in video by extracting and combining multi-features. With the background reconstruction method we proposed, the moving objects are separated as sub images from the background, we first extract some valuable features from each sub image, especially the topological information. Then, features are applied to a strong classifier which is accumulated with weak feature classifiers. After that, by the initial matching of moving objects, we extract their kinematical features to reinforce the matching method. Finally, experimental results show the effectiveness of the novel algorithm.

Nucleosome Positioning with Set of Key Positions and Nucleosome Affinity.

The formation and precise positioning of nucleosome in chromatin occupies a very important role in studying life process. Today, there are many researchers who discovered that the positioning where the location of a DNA sequence fragment wraps around a histone octamer in genome is not random but regular. However, the positioning is closely relevant to the concrete sequence of core DNA. So in this paper, we analyzed the relation between the affinity and sequence structure of core DNA, and extracted the set of key positions. In these positions, the nucleotide sequences probably occupy mainly action in the binding. First, we simplified and formatted the experimental data with the affinity. Then, to find the key positions in the wrapping, we used neural network to analyze the positive and negative effects of nucleosome generation for each position in core DNA sequences. However, we reached a class of weights with every position to describe this effect. Finally, based on the positions with high weights, we analyzed the reason why the chosen positions are key positions, and used these positions to construct a model for nucleosome positioning prediction. Experimental results show the effectiveness of our method.