In this paper, a new algorithm for function approximation is proposed to obtain better generalization performance and faster convergent rate. The new algorithm incorporates the architectural constraints from a priori ...
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Video-based gait recognition is a challenging problem in computer vision. In this paper, fractal scale wavelet analysis is applied to describe and automatically recognize gait. Fractal scale based on wavelet analysis ...
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Video-based gait recognition is a challenging problem in computer vision. In this paper, fractal scale wavelet analysis is applied to describe and automatically recognize gait. Fractal scale based on wavelet analysis represents the self-similarity of signals, and improves the flexibility of wavelet moments. Optimal wavelets based on generalized multi-resolution analysis are used to improve the recognition rate. Descriptors of fractal scale are translation, scale and rotation invariant. Moreover, a combination of fractal scale and wavelet moments improves the recognition rate. Experiments show that the proposed descriptor is efficient for gait recognition
The current color transfer methods always use statistics as transfer function and can not deal with images with lower similarity. In this paper, a section by section color transfer method is presented, in which all th...
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The current color transfer methods always use statistics as transfer function and can not deal with images with lower similarity. In this paper, a section by section color transfer method is presented, in which all the source images and reference images are segmented into a series of homogeneous regions, in which variations between classes are big and variations within classes are small, and then the color between the corresponding regions are transferred. The experiments show that the algorithm is efficient, the results are satisfactory, and it can be applied to complicated images with lower similarity.
Based on the fusion of color and gradient features, this paper implements a novel approach to real-time background subtraction. Firstly, an energy function is defined based on the fusion of color and gradient features...
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Based on the fusion of color and gradient features, this paper implements a novel approach to real-time background subtraction. Firstly, an energy function is defined based on the fusion of color and gradient features. Secondly, the graph cuts based algorithm is employed to minimize energy function and segment the foreground. Finally, average optical flow is used to make inference about the validity of foreground regions, background models are then updated. The experimental results of different real scenes show that the proposed approach can produce real-time detection and promising results.
This paper proposes an edge detection scheme based on Fresnel diffraction mode. Since Fresnel diffraction is mathematically complex, it is simplified into a linear convolution filter. Experiments on images are compare...
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This paper proposes an edge detection scheme based on Fresnel diffraction mode. Since Fresnel diffraction is mathematically complex, it is simplified into a linear convolution filter. Experiments on images are compared with the Laplacian of Gaussian, Sobel and Canny edge detection algorithms. The experimental results indicate that the new detector's result is comparable to Canny detector and agree more with human's recognition. And it also can get an even better edge map on some regions which contain abundant local details or some tiny changes.
The Resource Space Model (RSM) is a semantic data model based on orthogonal classification semantics for effectively managing various resources in interconnection environment. In parallel with the integrity theories o...
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Linear classifiers are of great significance in the classification field. In this paper, with meticulous studies on the linear classifiers, we gained a general framework for constructing fast trained linear classifier...
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To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its nei...
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ISBN:
(纸本)1595930515
To improve search efficiency and reduce unnecessary traffic in Peer-to-Peer (P2P) networks, this paper proposes a trust-based probabilistic search algorithm, called preferential walk (P-Walk). Every peer ranks its neighbors according to searching experience. The highly ranked neighbors have higher probabilities to be queried. Simulation results show that P-Walk is not only efficient, but also robust against malicious behaviors. Furthermore, we measure peers' rank distribution and draw implications.
The analytical study of a large scale nonlinear neural network is an uneasy *** try to analyze the function of neural systems by probing into the fuzzy logical framework of the neural ceUs'dynamical *** papers inv...
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ISBN:
(纸本)0780394224
The analytical study of a large scale nonlinear neural network is an uneasy *** try to analyze the function of neural systems by probing into the fuzzy logical framework of the neural ceUs'dynamical *** papers investigate the relation between fuzzy logic and neural *** most investigations focus on finding new function of neural system by combining fuzzy logical and neural system. In this paper,a novel approach is used to understand the nonlinear dynamic characteristics of neural system by analyzing the fuzzy logic framework of neural *** is the only way to understand the behavior of a large scale nonlinear neural *** abstracting the fuzzy logical framework of a neural cell,our analysis enables the delicate design of network *** an example,a difficulty task to build a recurrent network model of primary visual cortex by common dynamical analysis can be easily completed by this kind approach.
The key issue of Peer Data Management Systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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