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Unsupervised Linear Discriminant Analysis

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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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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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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An Efficient Method for Face Localization and recognition in Color images

An Efficient Method for Face Localization and Recognition in...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Farshid Hajati Karim Faez Saeed Khoshfetrat Pakazad Pattern Recognition and Image Processing Laboratory EE Department Amirkabir University of Technology슠 Tehran Iran Electrical Engineering Department Amirkabir University of Technology슠 Tehran Iran

This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.

关键词： Face recognition image recognition Color Face detection Skin Eyes Principal component analysis Humans Feature extraction Neural networks

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A DSP/FPGA - Based Parallel Architecture for Real-time image processing

A DSP/FPGA - Based Parallel Architecture for Real-time Image...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Luxin Yan Tianxu Zhang Sheng Zhong Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

A DSP/FPGA-based parallel architecture oriented to real-time image processing applications is presented. The architecture is structured with high performance DSPs interconnected by FPGA. Within FPGA a FIFO interconnection network and the specific data communication protocol are implemented, which interconnect 3 DSPs (TMS320C6414) effectively. The measured performances in the prototype with the proposed parallel architecture, including inter-DSP data communication performance and system computing capacity, show high data transfer bandwidth (up to 400 Mbytes/s) with low latency as well as high image processing performance, which achieve a good balance for parallel image processing

关键词： Digital signal processing Field programmable gate arrays Parallel architectures image processing Data communication Computer architecture Multiprocessor interconnection networks Protocols Performance evaluation Prototypes

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Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology

Dualband Infrared Images Target Detection Algorithm Based on...

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2006 8th International Conference on Signal processing

作者： Yuqiu Sun Jinwen Tian Jian Liu School of Information and Mathematics Yangtze University State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and Technology

According to the features of mid-wave and long-wave infrared images,they are decomposed into morphology pyramid respectively based on the new multiscale mathematical morphology filters proposed in the *** features such as local maximum gray level and average gradient strength of every image are extracted at each level of morphology *** dualband infrared images based on fusion rule put forward in the paper,and then reconstruct original image and detect target using contrast threshold *** experiment results show that dualband infrared images target detection algorithm based on multiscale morphology algorithm is better than use mid-wave or long-wave infrared images detect targets alone.

关键词： image Dualband Infrared images Target Detection Algorithm Based on Multiscale Morphology Target

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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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