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SAr image despeckling based on edge detection and nonsubsampled second generation bandelets

Journal of Systems Engineering and Electronics 2009年第3期20卷 519-526页

作者： Zhang Wenge~(1,2),Liu Fang~(1,2),Jiao Licheng~(2,3)& Gao xinbo~(2,3) 1.School of Computer Science and Technology,xidian univ.,xi’an 710071,p.r.china 2.Key Lab.of intelligent perception and Image Understanding of Ministry of Education of china,xi’an 710071,p.r.china 3.inst,of intelligent information processing,xidian univ.,xi’an 710071,p.r.china School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of intelligent perception and Image Understanding of Ministry of Education of china Xi'an 710071 P. R. China Key Lab. of intelligent perception and Image Understanding of Ministry of Education of china Xi'an 710071 P. R. China inst. of intelligent information processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar （SAr） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAr image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.

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Artificial immune kernel clustering network for unsupervised image segmentation

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progress in Natural Science:Materials International 2008年第4期18卷 455-461页

作者： Wenlong Huang , Licheng Jiao institute of intelligent information processing, xidian univ.rsity, xi’an 710071, china institute of intelligent information processing Xidian University Xi’an 710071 China

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the sup- port vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coeffcient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algo- rithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predfined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAr images, and encouraging experimental results are obtained.

关键词： Artificial immune network Kernel mapping Nonsubsampled contourlet transform Unsupervised image segmentation

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Immunodominance and clonal selection inspired multiobjective clustering

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progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong Key Laboratory of intelligent perception and Image Understanding of the Ministry of Education of china, institute of intelligent information processing, xidian univ.rsity, xi’an 710071, china [a]Key Laboratory of intelligent perception and Image Understanding of the Ministry of Education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the pESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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A Novel rule Induction Algorithm

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Chinese Journal of Lasers 2001年第6期10卷 28-32页

作者： ZHENG Jianguo LIU Fang WANG Lei JIAO Licheng (Key Lab for radar Signal processing, xidian univ., xi′an 710071, china) 西安电子科技大学雷达信号处理国家重点实验室陕西西安 710071

Knowledge discovery in databases is concerned with extracting useful information from databases, and the immune algorithm is a biological theory-based and globally searching algorithm. A specific immune algorithm is designed for discovering a few interesting, high-level prediction rules from databases, rather than discovering classification knowledge as usual in the literatures. Simulations show that this novel algorithm is able to improve the stability of the population, increase the holistic performance and make the rules extracted have higher precision.

关键词： Immune algorithms knowledge discovery in databases data mining

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Memetic computation based on regulation between neural and immune systems: the framework and a case study

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Science china(information Sciences) 2010年第8期53卷 1519-1527页

作者： GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie Key Lab of intelligent perception and Image Understanding of Ministry of Education of china, institute of intelligent information processing, xidian univ.rsity, xi’an 710071, china 1. Key Lab of intelligent perception and Image Understanding of Ministry of Education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.

关键词： memetic computation artificial immune system Lamarckian learning genetic algorithm numerical optimization

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A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,xiaozheng Deng Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china,institute of intelligent information processing,xidian univ.rsity,xi’an 710071,china [a]Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game replicator dynamics Quantum-inspired optimization Artificial immune system

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Immune secondary response and clonal selection inspired optimizers

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progress in Natural Science:Materials International 2009年第2期19卷 237-253页

作者： Maoguo Gong a,Licheng Jiao a,Lining Zhang a,Haifeng Du b a Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china,institute of intelligent information processing,xidian univ.rsity,xi’an 710071,china b School of public policy and Administration,xi’an Jiaotong univ.rsity,xi’an 710049,china [a]Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China [b]School of Public Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SrCpA) and secondary response clonal multi-objective algorithm (SrCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SrO) are the main operators of SrCpA and SrCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SrO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SrCpA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SrCpA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCpA,BGA and AEA,SrCpA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SrCMOA combines the pareto-strength based fitness assignment strategy,CSO and SrO to solve multi-objective optimization problems. The performance comparison between SrCMOA,NSGA-Ⅱ,SpEA,and pAES based on eight well-known test problems shows that SrCMOA has better performance in converging to approximate pareto

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms

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intelligent multi-user detection using an artificial immune system

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Science in china(Series F) 2009年第12期52卷 2342-2353页

作者： GONG MaoGuo, JIAO LiCheng, MA Wenping & MA JingJing Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china institute of intelligent information processing, xidian univ.rsity, xi’an 710071, china 1. Key Laboratory of intelligent perception and Image Understanding of Ministry of Education of china Xidian University Xi’an 710071 China 2. institute of intelligent information processing Xidian University Xi’an 710071 China

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

关键词： artificial immune systems clonal selection multi-user detection code-division multiple-access genetic algorithm

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Shared latent dynamical structure for three-dimensional human pose estimation

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Science china(information Sciences) 2011年第7期54卷 1375-1382页

作者： TONG MingLei 1,HAN Hong 2 & ZHU Wu 1 1 School of Computer and information,Shanghai univ.rsity of Electric power,Shanghai 201300,china 2 institute of intelligent information processing,xidian univ.rsity,xi’an 710071,china 1. School of Computer and information Shanghai University of Electric Power Shanghai 201300 China2. Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

In this paper,a shared latent dynamical model (SLDM) and its application in tracking 3D human motion from monocular videos are proposed by combining the ideas of Gaussian processes dynamical model with shared latent *** tracking in high-dimensional space,SLDM can map state space and observation space to a shared latent space of low dimensionality with associated *** off-line training,three mappings,including dynamical mapping in latent space and mappings from the latent space to both state space and observation space,are *** model can separate traditional human motion estimation in highdimensional space into two steps: In the first step,the shared latent dynamical variables are estimated;in the second step,the human pose of high dimension is *** in human motion tracking from monocular videos using simulations and real images demonstrate that this human tracking method is efficient.

关键词： visual media shared latent dynamical model 3D human pose estimation

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Stability of discrete Hopfield neural networks with delay

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Journal of Systems Engineering and Electronics 2005年第4期16卷 937-940页

作者： Ma runnian 1,2 , Lei Sheping3 & Liu Naigong41. Telecommunication Engineering inst., Air Force Engineering univ., xi’an 710071, p. r. china 2. Key Lab of information Sciences and Engineering, Dalian univ., Dalian 111662, p. r. china 3. School of Humanity Law and Economics, Northwestern polytechnical univ., xi’an 710072, p. r. china 4. Science inst., Air Force Engineering univ., xi’an 710051, p. r. china Telecommunication Engineering inst. Air Force Engineering Univ. XT an 710071 P. R. China Key Lab of information Sciences and Engineering Dalian Univ. Dalian 111662 P. R. China School of Humanity Law and Economics Northwestern Polytechnical Univ. XT an 710072 P. R. China Science inst. Air Force Engineering Univ. XT an 710051 P. R. China

Discrete Hopfield neural network with delay is an extension of discrete Hopfield neural network. As it is well known, the stability of neural networks is not only the most basic and important problem but also foundation of the network＇s applications. The stability of discrete HJopfield neural networks with delay is mainly investigated by using Lyapunov function. The sufficient conditions for the networks with delay converging towards a limit cycle of length 4 are obtained. Also, some sufficient criteria are given to ensure the networks having neither a stable state nor a limit cycle with length 2. The obtained results here generalize the previous results on stability of discrete Hopfield neural network with delay and without delay.

关键词： discrete Hopfield neural network with delay stability limit cycle.

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