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Image Enhancement Based on Brushlet Transform

电子学报(英文版) 2005年第1期14卷 106-110页

作者： HUXinwei YANGXin InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030China

In this paper, the method of image enhancement based on Brushlet transform is discussed. One favorable characteristic of brushlet is the good localization in both time and frequency domain so it allows treatment for a particular range of data. Furthermore, brushlet can be implemented with faster speed than wavelet transform while preserving the same quality thanks for folding technique and Fast Fourier transform (FFT). The innovation ofthis paper is that after first transforming the image from raw data into brushlet coefficients, we will apply a new nonlinear algorithm to the discrete coefficients. The algorithm is described by a curve which takes the main function of image enhancement. Compared to classical methods, the method presented in this paper not only improves the calculation speed but also provides a high-quality image.

关键词：图象增强非线性 Brushlet变换图像处理

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Phase transitions in a new car-following traffic flow model

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Chinese Physics B 2005年第3期14卷 576-582页

作者：李莉施鹏飞 InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030China

In this paper, we investigate the performance of the well-known optimal velocity car-following model (the OVM) with numerical simulation in describing the acceleration process that is induced by the motion of a leading car with a pre-specified speed profile. Results show that this model is to some extent deficient in performing this process. Modification of the OVM to overcome the deficiency is demonstrated. The linear stability for the modified model is analysed. If the linear stability condition can not be satisfied, phase transitions occur on varying the initial homogeneous headway of the traffic flow.

关键词： car-following models linear stability phase transitions

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Model update mechanism for mean-shift tracking

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Journal of Systems Engineering and Electronics 2005年第1期16卷 52-57页

作者： PengNingsong YangJie LiuErqi InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China//InstituteofElectronicsandInformationHenanUniversityofScienceandTechnologyLuoyang471039P.R.China InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China TheSecondAcademyofChinaAerospaceScienceandIndustryCorporation Beijing100854P.R.China

In order to solve the model update problem in mean-shift based tracker, a novel mechanism is proposed. Kalman filter is employed to update object model by filtering object kernel-histogram using previous model and current candidate. A self-tuning method is used for adaptively adjust all the parameters of the filters under the analysis of the filtering residuals. In addition, hypothesis testing servers as the criterion for determining whether to accept filtering result. Therefore, the tracker has the ability to handle occlusion so as to avoid over-update. The experimental results show that our method can not only keep up with the object appearance and scale changes but also be robust to occlusion.

关键词： mean-shift tracking model update Kalman filter hypothesis testing.

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Geodesic Distance for Support Vector Machines

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自动化学报 2005年第2期31卷 202-208页

作者：全勇杨杰 InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030

When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic *** introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.

关键词：测地距离支持向量机分类算法核心函数机器学习

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Watermarking for the Printed Images

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电子学报(英文版) 2004年第3期13卷 544-547页

作者： LIGang YANGJie ZHUYitan InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030China

This paper describes a new watermarking technique for printed images, using the texture synthesis method. In this aspect, a few techniques have been proposed, but those techniques are all based on FMS (Frequency modulated screening), which means high cost and additional difficulties in practice. On the other side the technique in this paper is based on AMS (Amplitude modulated screening). Ink dots are considered as texture patterns, and texture analysis is used to generate two corresponding texture patterns to describe ?A"1" and "0". Another advantage of this technique is that the robustness and the unobtrusiveness of watermark can be easily controlled. Finally, the result of a simulation experiment is reported, which demonstrate that the method in this paper is effective and potential.

关键词：数字水印 AMS 印刷图像纹理合成

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Research on Wavelet-Based Algorithm for Image Contrast Enhancement

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Wuhan University Journal of Natural Sciences 2004年第1期9卷 46-50页

作者： WuYing-qian DuPei-jun ShiPeng-fei InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030China

A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches. Key words wavelet transform - image contrast enhancement - multiscale analysis CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (69931010) Biography: Wu Ying-qian (1974-), male, Ph. D, research direction: image processing, image compression and wavelet.

关键词： wavelet transform image contrast enhancement multiscale analysis

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Efficient face recognition method based on DCT and LDA

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Journal of Systems Engineering and Electronics 2004年第2期15卷 211-216页

作者： ZhangYankun LiuChongqing InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China

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.

关键词： face recognition discrete cosine transform linear discriminant analysis principal component analysis.

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Blind separation of sources in nonlinear convolved mixture based on a novel network

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Journal of Systems Engineering and Electronics 2004年第4期15卷 497-501页

作者： HuYing YangJie Shenli InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China

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.

关键词： blind separation feedback architecture maximum entropy nonlinear convoluted mixture

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Image reconstruction for brain CT slices

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Journal of Systems Engineering and Electronics 2004年第3期15卷 382-386页

作者：吴建民施鹏飞 InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China

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.

关键词： filter AFCM segmentation contour cell array structure animation.

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Video segmentation using multiple features based on EM algorithm

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Journal of Systems Engineering and Electronics 2004年第4期15卷 656-662页

作者： ZhangFengchao YangJie LiuErqi InstituteofImageProcessingandPatternRecognition ShanghaiJiaotongUniversityShanghai200030P.R.China TheSecondAcademy ChinaAerospaceofScienceandIndustryCorporationBeijing100854P.R.China

Object-based video segmentation is an important issue for many multimedia applications. A video segmentation method based on EM algorithm is proposed. We consider video segmentation as an unsupervised classification problem and apply EM algorithm to obtain the maximum-likelihood estimation of the Gaussian model parameters for model-based segmentation. We simultaneously combine multiple features (motion, color) within a maximum likelihood framework to obtain accurate segment results. We also use the temporal consistency among video frames to improve the speed of EM algorithm. Experimental results on typical MPEG-4 sequences and real scene sequences show that our method has an attractive accuracy and robustness.

关键词： EM algorithm maximum-likelihood estimation video object segmentation

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