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IEEE Conference on Computer Vision and pattern recognition

作者： Menglong Yang Yiguang Liu Longyin Wen Zhisheng You Stan Z. Li Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense School of Aeronautics and Astronautics & Computer Science Sichuan University Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479951192

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" ispresented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Target tracking Probabilistic logic Approximation algorithms Cameras Proposals Computational modeling

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Safety and health monitoring of bridge construction based on wireless sensor network

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2014年第3期9卷 257-268页

作者： Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University Chengdu 610106 China University Key Laboratory of Pattern Recognition and Intelligent information Processing Chengdu 610106 China The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu 610000 China

The existing safety and health monitoring methods for bridge construction are mainly manual monitoring and wired monitoring with many disadvantages, such as low efficiency, poor accuracy, great implementation difficulty, short bridge age, etc. To solve such problems, the paper provides a design scheme for the bridge construction safety and health monitoring System based on wireless sensor networks. In the system, various sensors are mainly used to collect data about the wind, temperature, traffic load, humidity, static position, static displacement and external load response, and then send to the host by route and gateway nodes. After data analysis and processing, the safety of the bridge is evaluated to help the personnel make correct bridge safety precautions. With such advantages as low power demand, wireless transmission of monitoring data, etc., the system is suitable for large range of promotion. © 2014 SERSC.

关键词： Data handling

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New logistics distribution route dispatching mode based on genetic algorithm-ant colony algorithm

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Computer Modelling and New Technologies 2014年第10期18卷 313-318页

作者： Yi, Fasheng Li, Xiaoling Yuan, Jimin School of Computer Science and Technology Chengdu University Chengdu China University Key Laboratory of Pattern Recognition and Intelligent information Processing Chengdu China The Department Computer Engineering Chengdu Aeronautic Vocational and Technical College Chengdu China

For multi-target route optimization with constraint conditions, the mathematical model for logistics distribution route optimization is built to accelerate response speed of logistics enterprises to customers, improve service quality, and strengthen the satisfaction of customers, and a new algorithm with the combination of genetic and ant colony algorithms is proposed to solve the selection issues of such logistics route. Initial pheromone is formed with genetic algorithm, based on which the optimal solution is rapidly sought with ant colony algorithm, and complementary advantages are achieved between above two algorithms. Application examples and simulations are available for calculation, and the results show that such algorithm is practical and effective to optimize logistics distribution route.

关键词： Genetic algorithms

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3D model retrieval using density-based silhouette descriptor

3D model retrieval using density-based silhouette descriptor

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2nd International Conference on pattern recognition Applications and Methods, ICPRAM 2013

作者： Tang, Qi Yang, Xin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

ISBN: (纸本)9789898565419

In this paper we present a new content-based retrieval descriptor, density-based silhouette descriptor (DBS). It characterizes a 3D object with multivariate probability functions of its 2D silhouette features. The new descriptor is computationally efficient and induces a permutation property that guarantees invariance at the matching stage. Also, it is insensitive to small shape perturbations and mesh resolution. The retrieval performance on several 3D databases shows that the DBS provides state-of-art discrimination over a broad and heterogeneous set of shape categories.

关键词： Probability density function

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-labeling for brick product graphs

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Journal of Combinatorial Optimization 2014年第2期31卷 447-462页

作者： Shao, Zehui Xu, Jin Yeh, Roger K. School of Information Science and Technology Chengdu University Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu China School of Electronic Engineering and Computer Science Peking University Beijing China Department of Applied Mathematics Feng Chia University Taichung Taiwan

Let $$G=(V, E)$$ be a graph. Denote $$d_G(u, v)$$ the distance between two vertices $$u$$ and $$v$$ in $$G$$ . An $$L(2, 1)$$ -labeling of $$G$$ is a function $$f: V \rightarrow \{0,1,\cdots \}$$ such that for any two vertices $$u$$ and $$v$$ , $$|f(u)-f(v)| \ge 2$$ if $$d_G(u, v) = 1$$ and $$|f(u)-f(v)| \ge 1$$ if $$d_G(u, v) = 2$$ . The span of $$f$$ is the difference between the largest and the smallest number in $$f(V)$$ . The $$\lambda $$ -number of $$G$$ , denoted $$\lambda (G)$$ , is the minimum span over all $$L(2,1 )$$ -labelings of $$G$$ . In this article, we confirm Conjecture 6.1 stated in X. Li et al. (J Comb Optim 25:716–736, 2013) in the case when (i) $$\ell $$ is even, or (ii) $$\ell \ge 5$$ is odd and $$0 \le r \le 8$$ .

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Kernel based weighted group sparse representation classifier

Kernel based weighted group sparse representation classifier

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15th International Conference on Human-Computer Interaction, HCI International 2013

作者： Xu, Bingxin Guo, Ping Chen, C. L. Philip Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783642393419

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, SRC can not well classify the data when they are in the overlap feature space. In addition, SRC treats different samples equally and ignores the cooperation among samples belong to the same class. In this paper, a kernel based weighted group sparse classifier (KWGSC) is proposed. Kernel trick is not only used for mapping the original feature space into a high dimensional feature space, but also as a measure to select members of each group. The weight reflects the importance degree of training samples in different group. Substantial experiments on benchmark databases have been conducted to investigate the performance of proposed method in image classification. The experimental results demonstrate that the proposed KWGSC approach has a higher classification accuracy than that of SRC and other modified sparse representation classification. © 2013 Springer-Verlag.

关键词： Classification (of information)

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Optimal classification of epileptic EEG signals using neural networks and harmony search methods

Journal of Software

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Journal of Software 2014年第1期9卷 230-239页

作者： Gao, Xiao-Zhi Wang, Jing Tanskanen, Jarno M. A. Bie, Rongfang Wang, Xiaolei Guo, Ping Zenger, Kai College of Information Engineering Shanghai Maritime University China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Laboratory of Image Processing and Pattern Recognition Beijing Normal University China Department of Biomedical Engineering Tampere University of Technology Finland School of Foundational Education Peking University Health Science Center China

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training. © 2014 ACADEMY PUBLISHER.

关键词： Optimization

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On weak Sidon sequences

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Journal of Combinatorial Mathematics and Combinatorial Computing 2014年 91卷 107-113页

作者： Xu, Xiaodong Liang, Meilian Shao, Zehui Guangxi Academy of Sciences Nanning530007 China School of Mathematics and Information Science Guangxi University Nanning530004 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province China School of Information Science and Technology Chengdu University Chengdu610106 China

A sequence {ai |1 ≤ i ≤ k} of integers is a weak Sidon sequence if the sums ai + aj are all different for any i i |1 ≤ i ≤ k} such that 1 ≤ a1k ≤ n. Let the weak Sidon number G(k) = min{n | g(n) = k}. In this note, g(n) and G(k) are studied, and g(n) is computed for n ≤ 172, based on which the weak Sidon number G(k) is determined for up to k = 17.

关键词： Combinatorial mathematics

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Multiple kernel learning method using MRMR criterion and kernel alignment

Multiple kernel learning method using MRMR criterion and ker...

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20th International Conference on Neural Information processing, ICONIP 2013

作者： Wu, Peng Duan, Fuqing Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China

ISBN: (纸本)9783642420535

Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to choose the proper kernels in practical applications. In this paper, we propose a MKL method based on minimal redundant maximal relevance criterion and kernel alignment. The main feature of this method compared to others in the literature is that the selection of kernels is considered as a feature selection issue in the Hilbert space, and can obtain a set of base kernels with the highest relevance to the target task and the minimal redundancies among themselves. Experimental results on several benchmark classification data sets show that our proposed method can enhance the performance of MKL. © Springer-Verlag 2013.

关键词： Benchmarking

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Robust visual tracking based on online learning of joint sparse dictionary

Robust visual tracking based on online learning of joint spa...

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6th International Conference on Machine Vision, ICMV 2013

作者： Li, Qiaozhe Qiao, Yu Yang, Jie Bai, Li Key Laboratory of Ministry of Education for System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China School of Computer Science University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9780819499967

In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foreground and background objects respectively. An online dictionary learning method is developed to update the joint sparse dictionary by selecting both positive and negative bases from bags of positive and negative image patches/templates during tracking. A linear classifier is trained with sparse coefficients of image patches in the current frame, which are calculated using the joint sparse dictionary. This classifier is then used to locate the target in the next frame. Experimental results show that our tracking method is robust against object variation, occlusion and illumination change. © 2013 SPIE.

关键词： E-learning

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