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IEEE International Conference on Networking, Sensing and Control

作者： Zhipeng Li Xiaobo Gong Yuncai Liu Institute of Image Processing Pattern Recognition Shanghai JiaoTong University Shanghai China Department of Control Science and Technology Shanghai JiaoTong University Shanghai China

This paper attempts to introduce a velocity-separation difference model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it performs more realistically than others in the dynamical evolution of congestion. Furthermore, the proposed model is investigated with analytic and numerical method, with the finding that this model can demonstrate some complex physical features observed in real traffic such as the existence of three phases: free flow, coexisting flow, and jam flow; sudden flow drop; traffic hysteresis in transition between the free and the coexisting flow

关键词： Testing Traffic control Telecommunication traffic Road vehicles Mathematical model Intelligent transportation systems Acceleration Hysteresis Computer networks Computational modeling

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Unsupervised Linear Discriminant Analysis

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Journal of Shanghai Jiaotong University(Science) 2006年第1期E-11卷 40-42页

作者： TANG Hong FANG Tao SHI Peng-fei TANG Guo-an Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong Univ Shanghai 200030 China Three Gorges Project Command of Armed Police Hydropower Eng. Troops Hubei

An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-neares... 详细信息

关键词： linear discriminant analysis(LDA) unsupervised learning neighbor graph

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Geometrical probability covering algorithm

Geometrical probability covering algorithm

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2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

作者： Zhang, Junping Li, Stan Z. Wang, Jue Shanghai Key Laboratory of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9783540283126

In this paper, we propose a novel classification algorithm, called geometrical probability covering (GPC) algorithm, to improve classification ability. On the basis of geometrical properties of data, the proposed algorithm first forms extended prototypes through computing means of any two prototypes in the same class. Then Gaussian kernel is employed for covering the geometrical structure of data and used as a local probability measurement. By computing the sum of the probabilities that a new sample to be classified to the set of prototypes and extended prototypes, the classified criterion based on the global probability measurement is achieved. The proposed GPC algorithm is simple but powerful, especially, when training samples are sparse and small size. Experiments on several databases show that the proposed algorithm is promising. Also, we explore other potential applications such as outlier removal with the proposed GPC algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Algorithms

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Fractal Volume Rendering

Fractal Volume Rendering

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoliang Li Hongxing Qin Jie Yang Yuemin Zhu Institute of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China Research unit 5515 & Inserm unit 630 CREATIS/CNRS Villeurbanne France

Efficient visualization of large volumetric data is a challenge for image processing community. In this paper, we present a novel volume rendering algorithm based on the concept of fractal. It consists of dividing the volumetric data set into sub-blocks, calculating the 3D fractal coefficients of each sub-block, projecting them to 2D image plane, and generating sub-images through 2D inverse fractal transform. The final rendered image is then obtained by simply summing the sub-images. Compared to the conventional ray casting technique, the proposed fractal volume rendering (FVR) method presents the advantage of reducing time complexity as well as memory complexity while maintaining good rendering quality. Moreover, the progressive refinement is supported owing to the iterative convergent process of sub-image generation

关键词： Fractals Rendering (computer graphics) Data visualization image generation Casting image processing image converters Computed tomography Magnetic resonance imaging Monte Carlo methods

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Unsupervised Linear Discriminant Analysis

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Journal of Shanghai Jiaotong university(Science) 2006年第1期11卷 40-42页

作者：唐宏方涛施鹏飞唐国安 Inst.of Image Processing & Pattern Recognition Shanghai Jiaotong Univ Shanghai 200030 China Three Gorges Project Command of Armed Police Hydropower Eng.Troops Hubei 443133

关键词： linear discriminant analysis(LDA) unsupervised learning neighbor graph

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image Indexing Using Weighted Color Co-occurrence Matrix and Feature Selection

Image Indexing Using Weighted Color Co-occurrence Matrix and...

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IEEE Region 10 International Conference TENCON

作者： Dong Liang Edmund Y. Lam Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong China Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

In this paper, image indexing based on weighted color co-occurrence matrix (WCCM) feature and isolation parameter-based feature selection is introduced. In this method, isolation parameter (IP) is used to indicate the visual perception complexity and conduct feature selection for each query image. When indexing images from database in the reduced feature space, the similarities of diagonal elements and non-diagonal elements of CCM feature are weighted separately with different values based on the isolation parameters of query image and images from database. The experiments show that the proposed method provides better results than modified color co-occurrence matrix (MCCM) based method and sub-range cumulative histogram (SCH) based method

关键词： Indexing image databases Spatial databases image retrieval Visual databases Frequency Color Visual perception image processing pattern recognition

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RBF Network image Representation with Application to CT image Reconstruction

RBF Network image Representation with Application to CT Imag...

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International Conference on Computational Intelligence and Security

作者： Ping Guo Ming Hu Yunde Jia Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Beijing Institute of Technology Beijing China Beijing Normal University Beijing Beijing CN China

ISBN: (纸本)1424406048

Radial basis function (RBF) neural network can be used as a universal approximator. In this paper, we propose a novel method to apply RBF net to reconstruct 2-dimensional computerized tomography (CT) images from a small amount of projection data. In the method, the cross-sectional image is represented by a RBF network, the unknown cross-sectional image vector is replaced by the function of the network's weight vector. As proved by us, the line integral of the weight matrix can be calculated providing the projections of the CT image are known. The ART method can be employed to obtain the final reconstructed CT image. Experiments show that the proposed method can obtain the better reconstructed image than the filtered back projection (FBP), and it is also more efficient than ART method alone

关键词： Radial basis function networks image representation Computed tomography image reconstruction Subspace constraints Matrix converters Equations Application software Laboratories Neural networks

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Liver Fibrosis Identification Based on Ultrasound images

Liver Fibrosis Identification Based on Ultrasound Images

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Guitao Cao Pengfei Shi Bing Hu Institute of Image Processing and Pattern recognition Shanghai Jiaotong University Shanghai China Department of Ultrasound in Medicine Shanghai Sixth Hospital Shanghai Jiaotong University Shanghai China

Diagnostic ultrasound is one of useful and noninvasive tools for clinical medicine. However, due to its qualitative, subjective and experience-based nature, ultrasound images can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test on a group of 99 liver fibrosis images from 18 patients, as well as other 273 healthy liver images from 18 specimens

关键词： Liver Ultrasonic imaging Frequency Biomedical imaging Medical diagnostic imaging Feature extraction Fractals Entropy Support vector machines Support vector machine classification

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Ultrasonic Liver Characterization Using Phase Congruency

Ultrasonic Liver Characterization Using Phase Congruency

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Feature extraction plays an important role in the whole process of liver characterization. Because the ultrasonic scanner in use can be adjusted by different clinicians to produce optimal images, the ultrasound images captured sometimes can be greatly influenced by machine settings and further impact the classification result. In this paper, some experiments are made to try to extract the liver features using the 2D phase congruency, which invariant to changes in intensity or contrast, to try to avoid those problems. The effectiveness of our method tested on three classes of liver images shows the potential for physicians to quantify liver status in clinical diagnosis

关键词： Ultrasonic imaging Feature extraction Attenuation Liver diseases Cells (biology) Biopsy Testing Clinical diagnosis Support vector machines Radiofrequency identification

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Active Feature Models

Active Feature Models

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International Conference on pattern recognition

作者： G. Langs P. Peloschek R. Donner M. Reiter H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Department of Radiology Medical University of Vienna Austria

ISBN: (纸本)0769525210

In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. Experimental results and the comparison to AAMs on different data sets indicate that active feature models can improve search speed and result accuracy, considerably

关键词： Shape Active appearance model Biomedical imaging image texture image edge detection image texture analysis Computer graphics pattern recognition image processing Radiology

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