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Clonal selection algorithm based on anti-idiotype

Ruan Jian Xue Bao/Journal of Software 2009年第5期20卷 1269-1281页

作者： Zhang, Li-Ning Gong, Mao-Guo Jiao, Li-Cheng Ma, Wen-Ping Institute of Intelligent Information Processing Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an 710071 China

Based on the antibody clonal selection theory of immunology, an artificial immune system algorithm, clonal selection algorithm based on anti-idiotype (AICSA), is proposed to deal with complex multi-modal optimization problems by introducing the anti-idiotype. This algorithm evolves and improves the antibody population through clonal proliferation, anti-idiotype mutation, anti-idiotype recombination and clonal selection operation, which can perform global search and local search in many directions rather than one direction around the identical antibody simultaneously. Theoretical analysis proves that AICSA can converge to the global optimum. By introducing the anti-idiotype, AICSA can make the most of the structure information of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, AICSA is tested on four different types of functions and compared with the clonal selection algorithm and other optimization methods. Theoretical analysis and experimental results indicate that AICSA achieves a good performance, and is also an effective and robust technique for optimization. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Antibodies

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Needle segmentation in 3D ultrasound images based on phase-grouping

Needle segmentation in 3D ultrasound images based on phase-g...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhao, Siying Qiu, Wu Ming, Yuchi Ding, Mingyue Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China Institute of Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Hubei 10086 China School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Life Science and Technology Huazhong University of Science and Technology Hubei 10086 China

ISBN: (纸本)9780819478085

Three Dimensional (3D) ultrasound images can provide spatial information to help doctors locate the needle position precisely in ultrasound-guided surgery. In this paper, we present a method called "3D Phase-grouping" to segment the needle inside the patient. The method is an extension from 2D phase-grouping to the 3D case via a new mathematic model-the outer products of adjacent orientation vectors. Firstly, the voxels with the same outer products of the gradient orientation vectors of the adjacent points are divided into Line Support Regions (LSR). Then, the needle is extracted with the 3D Least-Squares line fitting method in the maximal LSR. Synthetic and 3D ultrasound phantom data were tested. The segmentation results were evaluated by the angular deviation, position deviation and computational time. Compared with the 3D Hough transform, 3D Phase-Grouping is more accurate and faster without using pre-information. The computational complexity and robustness of the algorithm remain to be our future research topics. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Needles

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Robust image modeling technique with a bioluminesence image segmentation application

Robust image modeling technique with a bioluminesence image ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Zhong, Jianghong Wang, Ruiping Tian, Jie Department of Biomedical Engineering School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Medical Image Processing Group Key Laboratory of Complex Systems and IntelligenceScience Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780819475138

A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the ***, it will be ***,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved. © 2009 SPIE.

关键词： Bioluminescence

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image enhancement based on intrascale dependencies of the second generation curvelet transform

Image enhancement based on intrascale dependencies of the se...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Hao, Hongxia Liu, Fang Jiao, Licheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478092

This paper presents a strong noise image enhancement method based on intrascale dependencies of the second generation curvelet transform. Observing that the immediate four neighbor coefficients bear the most important dependencies, we use spatial clustering property of the intrascale neighbor coefficients to separate noise and signal of interest, and to deal with them differently, i.e. to suppress noise and strengthen edges. Comparing our approach with Starck's enhancement model (Starck et al., 2003), we experimentally find that for high noise level images, our method outperforms the starck's system in noise suppression and signal strengthening and produces better enhancement results. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image enhancement

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Silhouette-Based 2D Human Pose Estimation

Silhouette-Based 2D Human Pose Estimation

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International Conference on image and Graphics (ICIG)

作者： Meng Li Tao Yang Runping Xi Zenggang Lin Shaanxi Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9781424452378

In this paper we present a novel silhouette-based method to estimate 2D human pose. It takes a pre-defined human skeleton model as the prior information and a video sequence as the data source, and estimates human pose in each frame by the following steps: Firstly, the Gaussian Mixture Background Model (GMM) is adopted to extract silhouette from an image and this silhouette will be the human body data set after treatment. Then the Distance Transform (DT) and Principal Component Analysis (PCA) are introduced to locate the base point of the human skeleton model, with which the human skeleton model is initialized automatically. Afterwards an iterative process, based on the Expectation Maximization (EM), is constructed out to cluster the human body data set and estimate the parameters of the skeleton iteratively. Finally the pose of human body is figured out in the form of a corresponding skeleton model. Extensive experiments show that this method is robust and precise, and is feasible to apply in the real-time system.

关键词： Humans Skeleton Biological system modeling Data mining Video sequences Principal component analysis Parameter estimation Robustness Computer vision Computer graphics

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Real-Time Camera Pose Estimation Based on Multiple Planar Markers

Real-Time Camera Pose Estimation Based on Multiple Planar Ma...

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International Conference on image and Graphics (ICIG)

作者： Rui Yu Tao Yang Jiangbin Zheng Xingong Zhang Shaanxi Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University China

Vision-based registration techniques for augmented reality(AR) systems have been the subject of intensive research recently due to their potential to accurately align virtual objects with the real world. The downfall of these vision-based approaches, however, is their high computational cost and lack of robustness. To address these shortcomings, a robust pose estimation algorithm based on artificial planar markers is adopted. This algorithm solves the problem of camera pose ambiguities and is able to draw a unique and robust solution. Experiments show the robustness and effectiveness of this method in the context of real-time AR tracking.

关键词： Cameras Robustness Iterative algorithms Feature extraction Algorithm design and analysis Force measurement Computer graphics Real time systems Laboratories Speech processing

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image Registration Based on Rectangle Pattern

Image Registration Based on Rectangle Pattern

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International Conference on image and Graphics (ICIG)

作者： Xingong Zhang Runping Xi Huiping Li Xiuwei Zhang Tao Yang Shaanxi Key Laboratory of Speech & Image Information Processing School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9781424452378

To against the complexity in finding feature-pair in image registration caused by traditional features: corners, lines and image edge, a novel method for image registration based on rectangle pattern is proposed. Unlike traditional features, a rectangle pattern can be described as its four vertexes and center, which can afford five pair-wise points for any kind of image transformation, and it holds stable in different weather condition, time and imaging way, which formed by building angular, widely found in aero-image. Firstly, image edge is detected by canny, and then distance transform is applied on the result of canny, by follows, a thresholding and mask convoluting are used on the distance transform result to avoid the interfering complex lines and edge. The center of the rectangles is obtained by clustering on the prior result, and then the four vertexes is calculated by geometric restrict on rectangle pattern. Consequently, we calculate the centers pair of the rectangles by their slope difference which aims to get the correct pair-wise points set between two images. Finally, we use the four vertexes pair and center pair of the rectangle pattern as the input of RANSAC algorithm, to solve an affine transform. The proposed algorithm is proved to be effective and accurate on the translation, rotation and scale between electro-optic(EO) images pair and SAR-EO images pair. 1.3 pixels of registration accuracy result is obtained in the experiment.

关键词： image registration image edge detection Computer graphics Laboratories Speech processing Information processing Computer science Pixel image processing Wavelet coefficients

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Automated Volumetric Measurement of Brain Structures and White Matter Lesions in Alzheimer's Disease and Mild Cognitive Impairment

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Alzheimer's & Dementia 2010年第4期6卷 S22-S23页

作者： Huali Wang Frithjof Kruggel Daniel Chang Min-Ying Su Xin Yu Peking University Institute of Mental Health Beijing China Key Laboratory for Mental Health Ministry of Health (Peking University) Beijing China Signal and Image Processing Lab Department of Biomedical Engineering University of California Irvine CA USA Tu & Yuen Center for Functional Onco-Imaging University of California Irvine CA USA

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Decentralized adaptive stabilization of stochastic nonlinear interconnected systems by state feedback

Decentralized adaptive stabilization of stochastic nonlinear...

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2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009

作者： Fan, Huijin Xie, Wenxiang Key Laboratory of Image Processing and Intelligent Control Dept. Control Science and Engineering Huazhong University of Science and Technology 430074 China Seagate Technology International Singapore Science Park 63 The Fleming Science Park Drive Singapore 118249 Singapore

ISBN: (纸本)9781424428007

This paper studies a class of stochastic nonlinear interconnected systemswith unknown parameters involved. By employing the stochastic Lyapunov-like theorem and the back-stepping design technique, a decentralized controller and parameter adaptive law are proposed by state-feedback, which can globally stabilize the overall system asymptotically in probability. © 2009 IEEE.

关键词： State feedback

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Interpolating fine texturess with fields of experts prior

Interpolating fine texturess with fields of experts prior

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IEEE International Conference on image processing

作者： Kai Guo Xiaokang Yang Rui Zhang Songyu Yu Hongyuan Zha Institute of Image Communication and Information Processing Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China College of Computing Georgia Institute of Technology Atlanta GA USA

Traditional image interpolation methods assume that the local spatial structure of the low-resolution (LR) and high-resolution (HR) images are approximately the same, and use edge information of the LR image to estimate the missing pixels. This assumption, however, no longer holds for natural images with fine and dense textures. Consequently, those methods cannot restore dense textures well and tend to generate over-fitting visual effects. In this paper, a learned HR image prior is exploited to overcome the problems. In particular, we use Fields of Experts (FoE) with student's t-distribution experts to model the prior, taking advantage of its representative ability of non-Gaussian natures in images. Then Maximum a Posterior (MAP) estimation incorporating FoE prior is used to estimate the missing pixels. Experimental results compared with traditional interpolation methods demonstrate that our method not only can recover fine details and produce superior PSNR values, but also avoid the visual over-fitting problems.

关键词： Interpolation Pixel image restoration Visual effects PSNR Layout image resolution image communication Information processing Biometrics

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