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30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Li, Guokuan Luo, Yu Deng, Wei Xu, Xiangyang Liu, Aihua Song, Enmin Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Minist Educ Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China Sixth Hosp Ultrasound Dept Wuhan Peoples R China

ISBN: (纸本)9781424418145

B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.

关键词： fatty liver B-scan ultrasound grayscale co-occurrence matrix SVM feature extraction

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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11th International Conference on Information Fusion, FUSION 2008

作者： Tajbakhsh, Nima Araabi, Babak N. Soltanianzadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Eng. University of Tehran Tehran Iran School of Cognitive Sciences IPM Tehran Iran Image Analysis Lab. Radiology Dept. Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)9783000248832

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Biometrics

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An intelligent decision combiner applied to noncooperative iris recognition

An intelligent decision combiner applied to noncooperative i...

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11th International Conference on Information Fusion, FUSION 2008

ISBN: (纸本)9783000248832

In despite of successful implementation of iris recognition systems, noncooperative recognition is still remained as an unsolved problem. Unexpected behavior of the subjects and uncontrolled lighting conditions as the main aspects of noncooperative iris recognition result in blurred and noisy captured images. This issue can degrade the performance of iris recognition system. In this paper, to address the aforementioned challenges, an intelligent decision combiner is proposed in which prior to perform decision fusion;an automatic image quality inspection is carried out. The goal is to determine whether captured decisions based on visible light (VL) and near infrared (NIR) images have enough reliability to incorporate into final decision making. Experimental results on the UTIRIS confirm the superior performance of the proposed combiner in comparison with other common nontrainable decision combiners whereas in all cases, the effectiveness of fusion approach makes it a reliable solution to noncooperative subjects' behavior and uncontrolled lighting conditions.

关键词： Decision making

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Data Fusion and Efficient Algorithm for Moving Target Tracking

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IFAC Proceedings Volumes 2008年第2期41卷 2406-2411页

作者： Guocheng Liu Yongji Wang Chinese Education Ministry Key Lab. for Image Processing and Intelligence Control Department of Control Science and Engineering Huazhong University of Science and Technology P.R. China (Tel: 0086-27-68772267

The principle of target tracking and data fusion techniques are discussed. To resolve high uncertainty that exists in sensors of mobile robots, one multi-sensor data fusion algorithm is presented. The algorithm is based on particle filter techniques, fuses the information coming from multiple sensors and merges different state space models. So it can be used to eliminate system and measurement noise and estimate value of position and headings of mobile robot. On simulation experiments, we compare different cases such as single sensors and multi-sensor data fusion, the results demonstrate the feasibility and effectiveness of this algorithm and exhibits good tracking performance.

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Automatic needle segmentation using 3D Gray-level Hough Transform in 3D ultrasound images

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Journal of Computational Information Systems 2008年第5期4卷 2021-2026页

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Image Processing and Intelligence Control Key Lab. of Education Ministry of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in 3D space and Local Dimensionality Estimator (GST). Different from the traditional Hough transformation the process of binarization is not needed, because the proposed algorithm adopts the original gray level image directly. We tested it with the synthetic data and water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1° and position deviation less than 1 mm. © 2008 Binary Information Press.

关键词： Hough transforms

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Real-time target tracking with particle filter in moving monocular camera

Real-time target tracking with particle filter in moving mon...

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5th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Guocheng Wang, Yongji Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Chinese Educ Minist Wuhan 430072 Peoples R China

ISBN: (纸本)9780819469502

In this paper, we propose an improved particle filter algorithm for real-time tracking a randomly moving target in dynamic environment with a moving monocular camera. For making the tracking task robustly and effectively, color histogram based target model is integrated into particle filter algorithm. Bhattacharyya distance is used to weight samples by calculating each sample's histogram with a specified target model and it makes the measurement matching and samples' weight updating more reasonable. In order to reduce sample depletion, the improved algorithm will be able to take the latest observation into account. The experimental results confirm that the method is effective even when the monocular camera is moving and the target object is partially occluded in a clutter background.

关键词： visual tracking particle filter state-space model

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Parameter design method of stator flux estimation for direct torque control using ADRC

Parameter design method of stator flux estimation for direct...

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2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007)

作者： Hu, Chanjuan Wani, Yubin Jiang, Xianglong Luo, Hui Deng, Guozhang Qin, Yi Wan, Shuyun Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Hubei Peoples R China Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China HuangGang Politech Coll Wuhan 430074 Peoples R China

ISBN: (纸本)9781424407361

In traditional Direct Torque control (DTC), speed regulator is widely based on conventional Proportional Integral Derivative (PID) controller, which has to depend on precise math model of the subject. However, PID cannot easily achieve swift response, small overshooting and fine speed control precision especially in low speed range. This paper introduces a nonlinear control tcchnology-Auto Disturbance Rejection controller (ADRC) into DTC to improve the speed adaptation capability and robustness, and a parameter design method of stator flux estimation is proposed. The simulation results prove the system has good control performance.

关键词： Torque control

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Color image filtering based on impulsive noise detection

Color image filtering based on impulsive noise detection

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MIPPR 2007: Automatic Target Recognition and image Analysis;and Multispectral image Acquisition

作者： Li, Dehua Jin, Lianghai Yao, Xingzhong Xiong, Caiquan State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Second Artillery Command College Wuhan 430012 China School of Computer Science Hubei University of Technology Wuhan 430068 China

ISBN: (纸本)9780819469502

This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures and the perceptual visual quality.

关键词： Color image processing

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Spectral clustering based null space linear discriminant analysis (SNLDA)

Spectral clustering based null space linear discriminant ana...

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11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007

作者： Wenxin, Yang Junping, Zhang Department of Electronic Engineering Fudan University Shanghai 200433 China Shanghai Key Lab. of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China

ISBN: (纸本)9783540717003

While null space based linear discriminant analysis (NLDA) obtains a good discriminant performance, the ability easily suffers from an implicit assumption of Gaussian model with same covariance each class. Meanwhile, mixture model discriminant analysis, which is a good way for processing issues on multiple subclasses in each class, depends on human experience on the number of subclasses and has a highly complex iterative process. Considering the cons and pros of the two mentioned approaches, we therefore propose a new algorithm, called Spectral clustering based Null space Linear Discriminant Analysis (SNLDA). The main contributions of the algorithm include the following three aspects: 1) Employing a new spectral clustering method which can automatically detect the number of clusters in each class. 2) Finding a unified null space for processing multi-subclasses issues with eigen-solution technique. 3) Refining the calculation of the covariance matrix in a single sample subclass. The experimental results show the promising of the proposed SNLDA algorithm. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Clustering algorithms

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AN IKONOS image FUSION PROCESS USING SUPPORT VALUE TRANSFORM

AN IKONOS IMAGE FUSION PROCESS USING SUPPORT VALUE TRANSFORM

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHENG ZHENG WENZHONG SHI JIAN LIU Institute of Intelligent Vision and Image Information Best College of Electrical Engineering and In Advanced Research Center for Spatial Information Technology Department of Land Surveying and Geo-In State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote *** this paper, we apply the support value transform (SVT) to Ikonos image *** fused saliency features are represented by support values and extracted by *** low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan *** fusing results on Ikonos MS + Pan data demonstrate that the proposed image fusion method is effective and efficient.

关键词： Mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) Ikonos image fusion

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