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Multisensor image adaptive fusion based on nonsubsampled contourlet

Jisuanji Xuebao/Chinese Journal of Computers 2009年第11期32卷 2227-2238页

作者： Chang, Xia Jiao, Li-Cheng Jia, Jian-Hua 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 School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen Jiangxi 333002 China

An adaptive fusion method of multisensor images based on nonsubsampled contourlet transform is proposed in this paper, which can select the fusion weights of the low-frequency coefficients adaptively via golden section algorithm. The nonsubsampled contourlet transform is a flexible multi-scale, multi-direction and shift-invariant image decomposition, which is suitable for representing images bearing abundant detail and directional information. This is employed for fusing the directional high-frequency coefficients. For the directional high-frequency coefficients, the higher adding level of the directional subbands is used to select the better coefficient for fusion. The nonsubsampled contourlet transform can also avoids introducing ringing artifacts to fused images compared to ordinary method. Experimental results show that the proposed method achieves better fusion efficiency compared to image fusion methods based on Laplacian pyramid transform, wavelet transform, stationary wavelet transform and contourlet transform respectively.

关键词： Contourlet transform

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Trapping in scale-free networks with hierarchical organization of modularity

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Physical Review E 2009年第5期80卷 051120-051120页

作者： Zhongzhi Zhang Yuan Lin Shuyang Gao Shuigeng Zhou Jihong Guan Mo Li School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai 200433 China Department of Computer Science and Technology Tongji University 4800 Cao’an Road Shanghai 201804 China Software School Fudan University Shanghai 200433 China

A wide variety of real-life networks share two remarkable generic topological properties: scale-free behavior and modular organization, and it is natural and important to study how these two features affect the dynamical processes taking place on such networks. In this paper, we investigate a simple stochastic process—trapping problem, a random walk with a perfect trap fixed at a given location, performed on a family of hierarchical networks that exhibit simultaneously striking scale-free and modular structure. We focus on a particular case with the immobile trap positioned at the hub node having the largest degree. Using a method based on generating functions, we determine explicitly the mean first-passage time (MFPT) for the trapping problem, which is the mean of the node-to-trap first-passage time over the entire network. The exact expression for the MFPT is calculated through the recurrence relations derived from the special construction of the hierarchical networks. The obtained rigorous formula corroborated by extensive direct numerical calculations exhibits that the MFPT grows algebraically with the network order. Concretely, the MFPT increases as a power-law function of the number of nodes with the exponent much less than 1. We demonstrate that the hierarchical networks under consideration have more efficient structure for transport by diffusion in contrast with other analytically soluble media including some previously studied scale-free networks. We argue that the scale-free and modular topologies are responsible for the high efficiency of the trapping process on the hierarchical networks.

关键词： Random processes

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Boid particle swarm optimisation

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International Journal of Innovative Computing and Applications 2009年第2期2卷 78-85页

作者： Cui, Zhihua Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Rd. Zhongguancun Haidian District Beijing 100190 China Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology No. 66 Waliu Rd. Wanbailin District Taiyuan Shanxi 030024 China

Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds' boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Alignment

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Sigma Set: A small second order statistical region descriptor

Sigma Set: A small second order statistical region descripto...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaopeng Hong Hong Chang Shiguang Shan Xilin Chen Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology Chinese Academy and Sciences China Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China Institute of Computing Technology Chinese Academy of Sciences China Institute for Digital Media Peking University China

Given an image region of pixels, second order statistics can be used to construct a descriptor for object representation. One example is the covariance matrix descriptor, which shows high discriminative power and good robustness in many computer vision applications. However, operations for the covariance matrix on Riemannian manifolds are usually computationally demanding. This paper proposes a novel second order statistics based region descriptor, named “Sigma Set”, in the form of a small set of vectors, which can be uniquely constructed through Cholesky decomposition on the covariance matrix. Sigma Set is of low dimension, powerful and robust. Moreover, compared with the covariance matrix, Sigma Set is not only more efficient in distance evaluation and average calculation, but also easier to be enriched with first order statistics. Experimental results in texture classification and object tracking verify the effectiveness and efficiency of this novel object descriptor.

关键词： Robustness Histograms Fractals Lighting Statistics Solids Computer science Power engineering and energy Mathematics Power engineering computing

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Several progress of semi-supervised learning

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Journal of information and Computational Science 2009年第1期6卷 211-217页

作者： Ding, Shifei Su, Chunyang Jia, Weikuan Jin, Fengxiang Shi, Zhongzhi School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China College of Plant Protection Shandong Agricultural University Taian 271018 China College of Geoinformation Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

In this paper we discuss several progress of semi-supervised learning, making emphasis on semi-supervised classification. First, we introduce the history and basic concept and methods of semi-supervised learning, then explain the algorithms of semi-supervised learning in details, and discuss the current situation of semi-supervised learning, at last we look forward to the development trend of semi-supervised learning. © 2009 Binary information Press.

关键词： Supervised learning

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A Novel Hebbian Rules Based Method for Computation of Sparse Coding Basis Vectors

A Novel Hebbian Rules Based Method for Computation of Sparse...

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International Conference on Natural Computation (ICNC)

作者： Baixian Zou Jun Miao Xiaoling Yang Lijuan Duan Yuanhua Qiao Department of Information Science and Technology College of Arts and Science of Beijing Union University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Tianjin Academy of Agricultural Science Tianjin China College of Computer Science and Technology Beijing Beijing China College of Applied Science Beijing University of Technology Beijing China

Sparse coding has high-performance encoding and ability to express images, sparse encoding basis vector plays a crucial role. The computational complexity of the most existing sparse coding basis vectors of is relatively large. In order to reduce the computational complexity and save the time to train basis vectors. A new Hebbian rules based method for computation of sparse coding basis vectors is proposed in this paper. A two-layer neural network is constructed to implement the task. The main idea of our work is to learn basis vectors by removing the redundancy of all initial vectors using Hebbian rules. The experiments on natural images prove that the proposed method is effective for sparse coding basis learning. It has the smaller computational complexity compared with the previous work.

关键词： Computer science Stability Space technology Collaboration Mathematical model Euclidean distance Extraterrestrial measurements Particle measurements Fitting Game theory

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Detection efficiency of cooperative spectrum sensing in cognitive radio network

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The Journal of China Universities of Posts and Telecommunications 2008年第3期15卷 1-7页

作者： CHEN Xing BIE Zhi-song WU Wei-ling Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Sensing the spectrum in a reliable and efficient manner is crucial to cognitive radio. To combat the channel fading suffered by the single radio, cooperative spectrum sensing is employed, to associate the detection of multiple radios. In this article, the optimization problem of detection efficiency under the constraint of detection probability is investigated, and an algorithm to evaluate the required radio number and sensing time for maximal detection efficiency is presented. To show the effect of cooperation on the detection efficiency, the proposed algorithm is applied to cooperative sensing using the spectral correlation detector under the Rayleigh flat fading channel.

关键词： cognitive radio spectrum sensing cooperative sensing cyclostationarity

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Downlink scheduling of multiuser MIMO systems with transmit beamforming

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The Journal of China Universities of Posts and Telecommunications 2008年第2期15卷 45-49页

作者： SONG Xing-hua WU Wei-ling Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

This article deals with downlink scheduling for multiuser multiple-input multiple-output （MIMO） systems, where the base station communicates with multiple users simultaneously through transmit beamforming. Most of the existing transmission schemes for multiuser MIMO systems focus on optimizing sum rate performance of the system. The individual quality of service （QoS） requirements （such as packet delay and minimum transmission rate for the data traffic） are rarely considered. In this article, a novel scheduling strategy is proposed, where we try to optimize the global system performance under individual QoS constraints. By performing scheduling into two steps, namely successive user selection and power allocation, the scheduler can achieve efficient resource utilization while maintaining the QoS requirements of all users. Extensive simulations and analysis are given to show the effectiveness of the proposed scheduler.

关键词： power allocation scheduling spatial divisionmultiple access spatial orthogonality transmit beamforming

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Decoding OvTDM with sphere-decoding algorithm

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The Journal of China Universities of Posts and Telecommunications 2008年第4期15卷 35-39页

作者： JIN Xin LI Dao-ben Key Laboratory of Information Processing and Intelligent Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Overlapped time division multiplexing （OvTDM） is a new type of transmission scheme with high spectrum efficiency and low threshold signal-to-noise ratio （SNR）. In this article, the structure of OvTDM is introduced and the sphere-decoding algorithm of complex domain is proposed for OvTDM. Simulations demonstrate that the proposed algorithm can achieve maximum likelihood （ML） decoding with lower complexity as compared to traditional maximum likelihood sequence demodulation （MLSD） or viterbi algorithm （VA）.

关键词： OvTDM MLSD sphere-decoding

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Fea-Accu cascade for face detection

Fea-Accu cascade for face detection

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IEEE International Conference on Image processing

作者： Shengye Yan Shiguang Shan Xilin Chen Wen Gao Chinese Academy and Sciences Beijing China Institute of Computing Technology Digital Media Research Center Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China School of EE & CS Peking University Beijing China

Aiming at unloading the high training time burden of the popular cascaded classifier, in this paper, a novel cascade structure called Fea-Accu cascade is proposed. In Fea-Accu cascade training, the times of feature selection are largely reduced by enhancing the correlation among different stage classifiers of the cascaded classifier. In detail, for each stage classifier, before selecting new features out, the features selected out by previous stage classifiers are reused through creating new corresponding weak classifiers. To verify the efficiency and effectiveness of the proposed method, experiment is designed on frontal face detection problem. The experimental results show that it can largely reduce the training time. A frontal face detector with state-of-the-art classification performance can be learned in less than 10 hours.

关键词： Face detection Detectors Content addressable storage Design methodology information processing Computer vision Cameras Robustness Boosting Computer architecture

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