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Ordination-fuzzy min-max neural network classifier on unlabelled pattern classification

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2007年第2期20卷 173-179页

作者： Hu, Jing Yang, Jing Gao, Jun Laboratory of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Center for Biomimetic Sensing and Control Research Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 China

An ordination-fuzzy min-max neural network (OFMM) based on non-metric multidimensional scaling (MDS) is proposed to solve the classification problems of unlabelled input pattern. Firstly, all the input patterns are sorted by MDS to get their similarity measures. Then these measures are used to supervise the following expansion and contraction stage of hyperboxes for classification. OFMM shows the improvements in the validity of unlabelled patterns classification, the network structure, and training time. The experimental results on standard dataset demonstrate that OFMM is a practical and effective classifier which is superior to the traditional general-fuzzy min -max neural network (GFMM).

关键词： Classifiers Fuzzy min-max neural network (FMMN) Multidimensional scaling (MDS) Similarity measures Unlabelled pattern

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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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A genetic algorithm for solving multi-constrained function optimization problems based on KS function

A genetic algorithm for solving multi-constrained function o...

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Congress on Evolutionary Computation

作者： Jianhua Xiao Jin Xu Zehui Shao Congfeng Jiang Linqiang Pan Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China School of Hydropower and Information Engineering Huazhong University of Science and Technology Wuhan China

In this paper, a new genetic algorithm for solving multi-constrained optimization problems based on KS function is proposed. Firstly, utilizing the agglomeration features of KS function, all constraints of optimization problems are agglomerated to only one constraint. Then, we use genetic algorithm to solve the optimization problem after the compression of constraints. Finally, the simulation results on benchmark functions show the efficiency of our algorithm.

关键词： Genetic algorithms Constraint optimization Mathematical model Testing Operations research Mathematics Industrial engineering Optimization methods Quadratic programming Robustness

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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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Multi-sensor moving target tracking using particle filter

Multi-sensor moving target tracking using particle filter

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IEEE International Conference on Robotics and Biomimetics

作者： Guocheng Liu Yongji Wang Chinese Education Ministry Key Laboratory for Image Processing and Intelligence Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hebei China

ISBN: (纸本)9781424417612;1424417619

The principle of target tracking and data fusion techniques are discussed. To resolve high uncertainty that exists in sensors of mobile robots, the cross-sensor and cross-modality (CSCM) 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 heading of mobile robot. On simulation experiments, we compare different cases such as single sensor and multi-sensor data fusion, the results demonstrate the feasibility and effectiveness of this algorithm and exhibits good tracking performance.

关键词： Target tracking Particle filters Mobile robots Sensor fusion Hidden Markov models Sensor systems intelligent robots intelligent sensors Noise measurement State estimation

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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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An Ikonos image fusion process using support value transform

An Ikonos image fusion process using support value transform

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6th International Conference on Machine Learning and Cybernetics

作者： Zheng, Sheng Shi, Wenzhong Liu, Jian China Three Gorges Univ Inst Intelligent Vis & Image Informat Best Coll Elect Engn & Informat Technol Yichang 443002 Peoples R China Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China Hong Kong Polytech Univ Adv Res Ctr Spatial Informat Technol Dept Land Surveying & Geo Informat Hong Kong Peoples R China Huazhong Univ Sci & Technol State Educ Commiss Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9781424409723

Panchromatic (Pan)-sharpening of multispectral (MS) bands is an important technique in various applications of satellite remote sensing. In this paper, We apply the support value transform (SVT) to Ikonos image fusion. The fused saliency features are represented by support values and extracted by SVT. The 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 image. The 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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Evaluation of BOLD Sensitivity Using a Realistic MRI Simulator

Evaluation of BOLD Sensitivity Using a Realistic MRI Simulat...

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Joint Meeting of the International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging (NFSI-ICFBI)

作者： Ali-Reza Mohammadi-Nejad G.-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of ECE University of Tehran Tehran Iran Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA

MRl simulation is a suitable method to evaluate and acquire understanding of the complex blood oxygenation level dependent (BOLD) effect. To study the observability of BOLD signal, we extended a MRI simulator. This simulator is capable of complete modeling of object (tissue) and main field inhomogeneities. We made a digital phantom in which the magnitude of fractional oxygenation (Y), hematocrit (Hct) values in red blood cells, the diameter of vessels, and also the position of blood vessel within the voxel can be changed. Then we created echo planar imaging (EPI) images of the phantom under resting and activated states using the simulator. Resulting images were analyzed using cross-correlation and t-test analysis for activation detection. Then the effect of various parameters on the detection of BOLD is investigated. These parameters include fractional oxygenation, spatial extent of BOLD effect and the location of vessel inside voxel. This simulation study provides a basis for planning experiments aimed at BOLD contrast detection with EPI pulse sequence.

关键词： Magnetic resonance imaging Red blood cells Medical simulation Biomedical imaging image analysis Magnetic susceptibility Analytical models Imaging phantoms Blood vessels Magnetic properties

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EDGE DETECTION USING SUPPORT VALUE TRANSFROM

EDGE DETECTION USING SUPPORT VALUE TRANSFROM

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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 JINWEN TIAN JIAN LIU Electronics and Information Engineering Department Huazhong University of Science and Technology W State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074 Electronics and Information Engineering Department Huazhong University of Science and Technology W

An innovative edge detection algorithm using the support value transform is presented in this *** on the support value transform, the multi scale support value images are extracted from the *** resolution of an image is directly related to the proper scale for edge detection, and the second level support value image is used to locate the edge positions by their zero *** experiments are carried out and sharp image edges are obtained from a variety of sample *** with many other existing methods, including LoG and Canny detectors, the proposed algorithm is superior to the LOG and Roberts approach.

关键词： Edge detection mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) zero crossings

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Adaptively wavelet-based image denoising algorithm with edge preserving

引用

Chinese Optics Letters 2006年第2期4卷 80-83页

作者：谭毅华田金文柳健 State Key Laboratory of Education Ministry for Image Processing and Intelligent Control

A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge intbrmation. This method is adaptive to local image details, and can achieve bet, ter performance than the methods of state of the art.

关键词： Adaptive algorithms Detectors Edge detection Information technology Noise abatement Pattern recognition Wavelet transforms

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