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Textural fabric defect detection using adaptive quantized Gray-level Co-occurrence Matrix and Support Vector Description Data

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Information Technology Journal 2012年第6期11卷 673-685页

作者： Bi, Mingde Sun, Zhigang Li, Yesong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology 1037 Luoyu Road Hubei 430074 Wuhan China

A new defect detection algorithm base on Support Vector Data Description (SVDD) is proposed. A fabric texture model is built on the gray-level histogram of textural fabric image. Two Gray-level Co-occurrence Matrix (GLCM) features are used to characterize the fabric texture. And an adaptive quantization scheme base on the texture mode is proposed to reduce the size of GLCM and reduce the computational complexity of feature extraction. Besides, twonew features are proposed to characterize the continuous property of the fabric defects. The SVDD classifier is used as a detector for defect detection. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the proposed detection algorithm. And a prototyped detection system is built to evaluate the real-time performance of the detection algorithm. © 2012 Asian Network for Scientific Information.

关键词： Data description

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Adaptive flocking of multi-agent systems with locally Lipschitz nonlinearity

Adaptive flocking of multi-agent systems with locally Lipsch...

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31st Chinese control Conference, CCC 2012

作者： Ningzi, Zhang Housheng, Su Chen, Michael Z. Q. Xiaofan, Wang Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9789881563811

This paper investigates adaptive flocking of multi-agent systems (MASs) with a virtual leader. All agents and the virtual leader share the same intrinsic nonlinear dynamics, which satisfies a locally Lipschitz condition and depends on both position and velocity information of the agent itself. Under the assumption that the initial network is connected, an approach to preserving the connectivity of the network is proposed. Based on the Lyapunov stability theory, an adaptive flocking control law is derived to make the MASs track the virtual leader without collision. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results. © 2012 Chinese Assoc of Automati.

关键词： Multi agent systems

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Displacement prediction model of landslide based on ensemble of extreme learning machine 1

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19th International Conference on Neural Information processing, ICONIP 2012

作者： Lian, Cheng Zeng, Zhigang Yao, Wei Tang, Huiming Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Hubei China School of Computer Science South-Central University for Nationalities Wuhan Hubei China Faculty of Engineering China University of Geosciences Wuhan Hubei China

ISBN: (数字)9783642344787

ISBN: (纸本)9783642344770

Based on time series analysis, total accumulative displacement of landslide is divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes of landslide displacement and inducing factors. In this paper, a novel neural network technique called the ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Trend component displacement and periodic component displacement are forecasted respectively, then total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. A case study of Baishuihe landslide in the Three Gorges reservoir area is presented to illustrate the capability and merit of our model. © 2012 Springer-Verlag.

关键词： Landslides

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Integrating Segmentation and Classification Accuracy for Accuracy Assessment in Object-based image Analysis

Integrating Segmentation and Classification Accuracy for Acc...

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International Conference on Audio, Language and image processing

作者： Nan Li Hong Huo Tao Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education Institute of Image Processing and PatternRecognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467301732

An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relationship between pixels and their corresponding segments according to the overlaps of segments and reference polygons. Then, two improved confusion matrices that take the segmentation errors into consideration are used: one for pixel-level classification results, and the other for object-level classification results. A final accuracy assessment combines the statistics of these two confusion matrices. The proposed method can be applied to segmentation scale selection in the hierarchical interpretation system. An experiment on a SPOT5 image demonstrates the effectiveness of this method for segmentation scale selection, which can guide the fusion of objects of different scales to obtain a higher accuracy.

关键词： segmentation image analysis Segmentation object orientation confusion matrix pixel

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A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance 12

A novel segmentation method for convex lesions based on dyna...

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Yu, Mali Huang, Qiliang Jin, Renchao Song, Enmin Liu, Hong Hung, Chih-Cheng School of Computer Science Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan Hubei China Radiology Department Second People's Hospital of Nanning Nanning China School of Computing and Software Engineering Southern Polytechnic State University GA United States

ISBN: (纸本)9781450308571

Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradient is used as an important factor in the cost function to attract the contours to the boundaries. Since medical images have their characteristics such as low contrast, blurred edges and high noises, the gradient operator cannot work well enough to achieve a satisfactory performance for boundary detection. We define the local intra-class variance and combine it with the dynamic programming method to replace the traditional gradient operation. Experiments on synthetic and X-ray images are carried out and the results are compared with Canny and fast multilevel fuzzy edge detection (FMFED) algorithms. It is demonstrated that the proposed method performs better on medical images with low contrast, blurred edges and high noises. In addition, 483 regions of interest of mammograms randomly extracted from DDSM of the University of South Florida are used to compare our proposed method with the plane-fitting and dynamic programming method (PFDP), and the normalized cut segmentation method (Ncut). The results demonstrate that our method is more accurate and robust than PFDP and Ncut. © 2012 ACM.

关键词： Dynamic programming

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A novel and fast method for precise license plate location from digital images based on fusion of salient features

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第19期6卷 511-519页

作者： Wang, Ran Wang, Guoyou Liu, Jianguo Chen, Zhong Shi, Yu Image Processing and Intelligent Key Laboratory of Ministry of Education of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 730074 China

The license plate location technique is an important image processing step in license plate recognition system. Vehicle license plates are distinguished from backgrounds using features proposed in existing literatures. However, the effect of location is quite affected by feature selection. In this paper, we propose a method of precise license plate location fusing salient features. The method is mainly divided into three steps. First, candidate license plate regions are detected using improved Harris corner feature with much less time than traditional method. Then, candidates are sifted to only retain license plates based on two salient features named color combination and mean difference which are first proposed in this paper. Finally, the license plates are located precisely according to the projection feature. In experiment, the proposed algorithm was tested with 1942 real images captured in different environment and the license plates are successfully located as 97.6% in average with only 109ms. The experiment results demonstrates the effectiveness and efficient of our algorithm.

关键词： image processing

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Person re-identification across multi-camera system based on local descriptors

Person re-identification across multi-camera system based on...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Qiao Huang Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Tracking the same person across multiple cameras is an important task in multi-camera systems. It is also desirable to re-identify the individuals who have been previously seen with a single-camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from video surveillance system. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient Bag of Features approach for image presentation. In this way, robustness against low resolution, occlusion and pose, viewpoint and illumination changes is achieved in a very fast way. We get promising results from the evaluation with situations where a number of individuals vary continuously from a multi-camera system.

关键词： Vocabulary Cameras Histograms Feature extraction image color analysis Support vector machine classification Kernel

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An Iterative Procedure for Multidimensional Realization By LFT Techniques

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IFAC Proceedings Volumes 2012年第13期45卷 723-727页

作者： Huijin Fan Li Xu Kose Uemura Shinya Matsushita Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Dept. Control Science & Engineering Huazhong University of Science and Technology P. R. China Dept. of Electronic and Information Systems Akita Prefectural University Akita Japan

In this paper, by utilizing Linear Fractional Transformation (LFT) techniques, an iterative realization procedure resulting in multidimensional ( n -D) Roesser state-space model is proposed. The philosophy employed here is to achieve an overall n -D realization by realizing relatively lower-dimensional systems with respect to (w.r.t.) less-number of variables iteratively. Symbolic examples are presented to illustrate the basic idea as well as the effectiveness of the proposed procedure.

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Analytical algorithm of program quaternion in the arbitrary spacecraft attitude-adjusting control

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Journal of Information and Computational Science 2012年第3期9卷 751-759页

作者： Ni, Shaobo Wu, Jianwu Liu, Lei Wang, Yongji Shan, Jiayuan Su, Mao School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China Beijing Aerospace Automatic Control Institute Beijing 100854 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

The analytical algorithm of program quaternion is studied, aiming at the problem of the arbitrary space vehicle attitude-adjusting control. The analytical constructor method of the program quaternion is provided for arbitrary given initial quaternion, initial angle velocity, terminal quaternion and terminal angle velocity. The presented method has advantages as: good adaptability, the initial and terminal conditions are both arbitrary;reduced operation, the calculation process involves the fundamental algebraic operation;direct optimization, the time consumption in the process of space vehicle attitude adjustment and the maximum angular velocity can be optimized directly by using the program quaternion model. Finally, according to the simulation results, the program quaternion tracking can be realized by a PD control law. © 2012 Binary Information Press.

关键词： Attitude control

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One-Class SVM assisted accurate tracking

One-Class SVM assisted accurate tracking

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Keren Fu Chen Gong Yu Qiao Jie Yang Irene Guy Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Chalmers tekniska hogskola Goteborg SE

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by One-Class SVM is bounded by a closed sphere, we propose a novel tracking method utilizing One-Class SVMs that adopt HOG and 2bit-BP as features, called One-Class SVM Tracker (OCST). Simultaneously an efficient initialization and online updating scheme is also proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods on providing accurate tracking and alleviating serious drifting.

关键词： Target tracking Support vector machines Training Feature extraction Face Boosting Semisupervised learning

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