Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It co...
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Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It contains filtering, fuzzy segmentation, matching method of contours, cell array structure and image animation. Experimental results have shown its validity. The innovation is matching method of contours and fuzzy segmentation algorithm of CT slices.
A semantics-based pre-fetching model is presented. This model predicts future requests based on latent intention that the user's current access path implies in semantics, rather than on temporal relationships, whi...
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A semantics-based pre-fetching model is presented. This model predicts future requests based on latent intention that the user's current access path implies in semantics, rather than on temporal relationships, which oversomes the limitation of previous pre-fetching approaches. The hidden Markov model (HMM) was employed for mining actual intention from access patterns. Experimental results show that the proposed pre-fetching model has better general performance.
The blind source separation (BSS) is an important task for numerous applications in signal processing, communications and array processing. But for many complex sources blind separation algorithms are not efficient be...
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The blind source separation (BSS) is an important task for numerous applications in signal processing, communications and array processing. But for many complex sources blind separation algorithms are not efficient because the probability distribution of the sources cannot be estimated accurately. So in this paper, to justify the ME(maximum enteropy) approach, the relation between the ME and the MMI(minimum mutual information) is elucidated first. Then a novel algorithm that uses Gaussian mixture density to approximate the probability distribution of the sources is presented based on the ME approach. The experiment of the BSS of ship-radiated noise demonstrates that the proposed algorithm is valid and efficient.
De-interlacing is very important when converting interlaced pictures to progressive pictures in format ***-formats digital broadcast and progressive display requires the de-interlacing technique. An adaptive weight de...
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De-interlacing is very important when converting interlaced pictures to progressive pictures in format ***-formats digital broadcast and progressive display requires the de-interlacing technique. An adaptive weight deinterlacing method is proposed. It combines motion compensation technique with directional-based spatio-temporal filter efficiently. Experiment results indicate that the method can keep edge continuity and sharpness effectively, reduce the artifacts in motion areas, and shows better visual performance when the estimated motion vectors are inaccurate.
A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the a...
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A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the application of ker nel method in decoupling multivariable outputfeedback controllers. Simulation results are presented to show the feasibility of the proposed technique.
Blind separation of independent sources from their nonlinear convoluted mixtures is a more realistic problem than from linear ones. A solution to this problem based on the Entropy Maximization principle is presented. ...
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Blind separation of independent sources from their nonlinear convoluted mixtures is a more realistic problem than from linear ones. A solution to this problem based on the Entropy Maximization principle is presented. First we propose a novel two-layer network as the de-mixing system to separate sources in nonlinear convolved mixture. In output layer of our network we use feedback network architecture to cope with convoluted mixtures. Then we derive learning algorithms for the two-layer network by maximizing the information entropy. Based on the comparison of the computer simulation results, it can be concluded that the proposed algorithm has a better nonlinear convolved blind signal separation effect than the H.H. Y' s algorithm.
It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use t...
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It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use the principal component analysis (PCA) to project an image into a lower dimensional space, then perform the LDA transform to extract discriminant feature. But some useful discriminant information to the following LDA transform will be lost in the PCA step. To overcome these defects, a face recognition method based on the discrete cosine transform (DCT) and the LDA is proposed. First the DCT is used to achieve dimension reduction, then LDA transform is performed on the lower space to extract features. Two face databases are used to test our method and the correct recognition rates of 97.5 % and 96.0 % are obtained respectively. The performance of the proposed method is compared with that of the PCA + LDA method and the results show that the method proposed outperforms the PCA + LDA method.
Vehicle occupants that are out-of-position can be deadly injured by the deployment of the air bag in a crash situation. In recent years many different sensors and systems have been proposed to detect the type of occup...
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Vehicle occupants that are out-of-position can be deadly injured by the deployment of the air bag in a crash situation. In recent years many different sensors and systems have been proposed to detect the type of occupant and the position of the occupant's head. This work presents a method for classification and occupant's head detection based on passive stereo vision. The proposed system uses depth surface analysis and scene statistics together with support vector machines for classification and selection of head candidates. Evaluation of the method shows 99% correct for classification and 98% correct for head detection, using large sets of image data, and image sequences recorded in a driving vehicle.
A color-based visual technique is described based on the mean shift image segmentation method providing relevant information for robust localization of the visible road area in Urban Intelligent Transportation System ...
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A color-based visual technique is described based on the mean shift image segmentation method providing relevant information for robust localization of the visible road area in Urban Intelligent Transportation System (U-ITS). The traffic image sequences are firstly trained to extract the background and then segmented into separated parts by the mean shift method as initialization, regions with the number of pixels not less than a threshold and with more uniform surfaces with the 'same' color compared to their environment are filtered as recognized road area. The algorithm given in this paper can present road area recognition with arbitrary shapes, which is fit for unstructured road applications in urban cities very well.
A new method for natural color image segmentation using integrated mechanism is proposed in this paper. Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to ...
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A new method for natural color image segmentation using integrated mechanism is proposed in this paper. Edges are first detected in term of the high phase congruency in the gray-level image. K-mean cluster is used to label long edge lines based on the global color information to estimate roughly the distribution of objects in the image, while short ones are merged based on their positions and local color differences to eliminate the negative affection caused by texture or other trivial features in image. Region growing technique is employed to achieve final segmentation results. The proposed method unifies edges, whole and local color distributions, as well as spatial information to solve the natural image segmentation problem. The feasibility and effectiveness of this method have been demonstrated by various experiments.
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