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2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010

作者： Li, Lei Ding, Wen-Yan Li, Jin-Yan College of Automation Nanjing University of Post and Telecommunications Pattern Recognition and Intelligence System Nanjing 210003 China

ISBN: (纸本)9781424455386

In this paper, a novel fuzzy support vector machine based image watermarking scheme is proposed. Since the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However, the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this article, Firstly, we split the given image into 8 * 8 block, then calculated for each sub-block of the texture features as input vectors to train the support vector machine. The image sub-block is divided into two categories (one category is "-1" represents a weak texture, the other is "+1" represents a strong texture),and we make the strong texture as more important category in this paper. Therefore, given its larger fuzzy membership than the weak texture's. From the relevant theory of watermarking technology we know that a strong local image texture can tolerate more of the watermark information. In order to enhance watermark robustness and embedding more watermark information in the host image, we improve the accuracy of classifying one class(a strong texture). If the data points belonging to the class of strong texture are incorrectly categorized into the weak texture class, the amount of embedded watermarks are reduced and robustness(the ability of resisting attacks) of the image is decreased. Results show that this algorithm has good robustness against common image attacks. © 2010 IEEE.

关键词： Vectors

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HIGH EFFICIENT MEMORY FETCH ARCHITECTURE FOR MOTION COMPENSATION OF VIDEO DECODER

HIGH EFFICIENT MEMORY FETCH ARCHITECTURE FOR MOTION COMPENSA...

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2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

作者： Ming Wu Chuang Zhang Jun Guo Pattern Recognition and Intelligence System Laboratory (PRIS Lab) Beijing University of Posts and Te Pattern Recognition and Intelligence System Laboratory (PRIS Lab) Beijing University of Posts and T

To improve the performance of the real-time video decoding under the power-constrained environment, an efficient memory fetch architecture for accessing the motion compensation memory is proposed. According to the feature of accessing the motion compensation memory, the architecture adopts the cache mechanism to buffer the reference data and chooses DMA to access external memories. The architecture can be applied for H.264/AVC, MPEG-4, AVS and other video decoding systems. Compared to the conventional memory fetch module, experimental results show that the proposed architecture reduces 12.8％~16.7％ video decoding cycles in H.264 decoding. The timing and the area of this design are both satisfied after synthesized.

关键词： H.264/AVC video decoder motion compensation reference data fetch cache

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A novel robustness image watermarking scheme based on fuzzy support vector machine

A novel robustness image watermarking scheme based on fuzzy ...

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International Conference on Computer Science and Information Technology (CSIT)

作者： Lei Li Wen-Yan Ding Jin-Yan Li Pattern Recognition and Intelligence System College of Automation Nanjing University of Posts and Telecommunications Nanjing China

In this paper, a novel fuzzy support vector machine based image watermarking scheme is *** the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this article, Firstly, we split the given image into 8 * 8 block, then calculated for each sub-block of the texture features as input vectors to train the support vector machine. The image sub-block is divided into two categories( one category is “-1” represents a weak texture, the other is “+1” represents a strong texture), and we make the strong texture as more important category in this paper. Therefore, given its larger fuzzy membership than the weak texture's. From the relevant theory of watermarking technology we know that a strong local image texture can tolerate more of the watermark information. In order to enhance watermark robustness and embedding more watermark information in the host image, we improve the accuracy of classifying one class(a strong texture). If the data points belonging to the class of strong texture are incorrectly categorized into the weak texture class, the amount of embedded watermarks are reduced and robustness(the ability of resisting attacks) of the image is decreased. Results show that this algorithm has good robustness against common image attacks.

关键词： Gaussian noise Watermarking Support vector machines Robustness

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EXTRACTION OF AFFINE INVARIANT FEATURES FOR SHAPE recognition BASED ON ANT COLONY OPTIMIZATION

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Journal of systems Engineering and Electronics 2011年第6期000卷

作者： Yuxing Mao 1 * Ching Y.Suen 2 and Wei He 1 1.State Key Laboratory of Power Transmission Equipment & system Security and New Technology Chongqing University Chongqing 400044 P.R.China 2.Centre for pattern recognition and Machine intelligence Concordia University Montreal QC H3G 1M8 Canada State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing 400044 P. R. China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQC H3G IM8 Canada

A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape ***,the centroid distance and azimuth angle of each boundary point are ***,with a prior-defined angle interval,all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating *** that,the centroid distance ratios (CDRs) of any two opposite contour points to the barycenter are achieved as the representation of the shape,which will be invariant to affine *** the angles of contour points will change non-linearly among affine related images,the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding *** core issue is how to determine the angle positions for sampling,which can be regarded as an optimization problem of path *** ant colony optimization (ACO)-based path planning model with some constraints is presented to address this ***,the Euclidean distance is adopted to evaluate the similarity of shape features in different *** experimental results demonstrate the efficiency of the proposed method in shape recognition with translation,scaling,rotation and distortion.

关键词： shape recognition affine transformation centroid distance ratio (CDR) ant colony optimization (ACO) path planning

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Noise estimation and reduction in microarray images

Noise estimation and reduction in microarray images

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2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

作者： Shao, Guifang Mi, Hong Zhou, Qifeng Luo, Linkai Institute of Pattern Recognition and Intelligence System Xiamen University Xiamen China College of Public Administration Zhejiang University Hangzhou China

ISBN: (纸本)9780769535074

Lots of error sources affect the microarray image quality, especially the noise. An image may contain different type noises which will produce distinct influence on image processing, so it doesn't need to remove all. This paper analyzed the affection of different noises to automatic gridding and proposed grid line number for quantitive evaluation. A new algorithm for noise reduction was developed, which included two parts: edge noise reduction and highly fluorescence noise reduction. Edge detection was executed on the vertical and horizontal projections of microarray image. Highly fluorescent noise was removed by linear replace, which is an easy and fast means. The algorithm was implemented and compared to other common noise reduction methods. Experiment results show the feasibility of the proposed approach. © 2008 IEEE.

关键词： Noise abatement

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A NOVEL SIGNATURE SEARCHING FOR INTRUSION DETECTION system USING DATA MINING

A NOVEL SIGNATURE SEARCHING FOR INTRUSION DETECTION SYSTEM U...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： YA-LI DING LEI LI HONG-QI LUO Pattern Recognition and Intelligence System College of Automation Nanjing University of Post and Telecommunications Nanjing 210003 China

Intrusion Detection system (IDS) has recently emerged as an important component for enhancing information system security. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. In this paper, we propose a novel signature searching to detect intrusion based on data mining, which is an improved Apriori algorithm. We evaluate the capability of this new approach with the data from KDD 1999 data mining competition. Our experimental results demonstrate the potential of the proposed method.

关键词： Intrusion detection Data mining Association rule Apriori algorithm Frequent itemset Scenario

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A multiple object tracking and detection algorithm using PMHT and FCM

A multiple object tracking and detection algorithm using PMH...

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MIPPR 2009 - Automatic Target recognition and Image Analysis: 6th International Symposium on Multispectral Image Processing and pattern recognition

作者： Shi, Wen-Jun Shi, Shang-Qing Wang, Xiao-Ping Wang, Deng-Wei Xu, Hai-Quan Graduate Students' Brigade of Naval Aeronautical Astronautical University Yantai 264001 China Department of Early Warning and Surveillance Intelligence Air Force Radar Academy Wuhan 430019 China Pattern Recognition and Intelligent System Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478061

An efficient algorithm based on PMHT and FCM is presented to track and detect the infrared multiple targets from the sequences of IR. Candidate targets can be obtained and saved by preprocessing IR data, and the true target can be detected from the above candidate targets through tracking those targets by PMHT algorithm and prior knowledge. Data association and target detection can be realized by FCM. At the same time, target' disappearing and new targets' emerging phenomena occasionally arise during target tracking. This problem can be solved by 'memorizing and filling'. The proposed approach is validated using actual infrared image sequences. Experiment results indicate high performance of the proposed method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Target tracking

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Noise Estimation and Reduction in Microarray Images

Noise Estimation and Reduction in Microarray Images

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Computer Science and Information Engineering, CSIE, WRI World Congress on FLAGGED

作者： Guifang Shao Hong Mi Qifeng Zhou Linkai Luo Institute of Pattern Recognition and Intelligence System Xiamen University Xiamen China College of Public Administration University of Zhejiang Hangzhou China

Lots of error sources affect the microarray image quality, especially the noise. An image may contain different type noises which will produce distinct influence on image processing, so it doesnpsilat need to remove all. This paper analyzed the affection of different noises to automatic gridding and proposed grid line number for quantitive evaluation. A new algorithm for noise reduction was developed, which included two parts: edge noise reduction and highly fluorescence noise reduction. Edge detection was executed on the vertical and horizontal projections of microarray image. Highly fluorescent noise was removed by linear replace, which is an easy and fast means. The algorithm was implemented and compared to other common noise reduction methods. Experiment results show the feasibility of the proposed approach.

关键词： Noise reduction Noise figure Fluorescence Optical noise Tail Image edge detection Image processing Filters Data mining DNA

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A proposed GM-GRNN model for the prediction of behavior in complex system

A proposed GM-GRNN model for the prediction of behavior in c...

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19th IASTED International Conference on Modelling and Simulation, MS 2008

作者： Pan, Wei Huang, Yupeng DeGaris, Hugo Pattern Recognition and Intelligent System Institute Information Science and Technology School Xiamen University China Artificial Intelligence Institute Information Science and Technology School Xiamen University China

ISBN: (纸本)9780889867420

This paper analyses the kernel of the General Regression Neural Network (GRNN) model in detail, and presents its deficiencies in the domain of complex systems forecasting. We import various aspects of the Broyden-Fletcher-Goldfarb- Shanno (BFGS) Quasi-Newton method and GM(1,h) algorithms to improve the kernel of the GRNN model. We then apply this modified model to the problem of unemployment forecasting in China, as an example of its ability to model time-varying environments.

关键词： Forecasting

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Clustering algorithm based on the grid-density and the spatial partition tree

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Xitong Gongcheng Lilun yu Shijian/system Engineering Theory and Practice 2008年第7期28卷 125-131+137页

作者： Zeng, Dong-Hai Mi, Hong Liu, Li-Feng Institute of Pattern Recognition and Intelligence System Xiamen University Xiamen 361005 China College of Public Administration Zhejiang University Hangzhou 310027 China

The density-based clustering algorithm is a sort of clustering analysis, its main merit is to discover arbitrary shape cluster and is insensitive to the noise data. This paper proposed a new clustering algorithm based on the grid density and the spatial partition tree CGDSPT. It is able to cluster data through dividing the data space into several unit cells. Some concepts, for example: the density, the bunch and so on, are defined on the unit cell. Then we established a spatial index structure for spatial division. The CGDSPT inherits the merit of the density-based clustering algorithm, moreover CGDSPT has the linear time-complexity, therefore it suits to the large-scale data mining. The theoretical analysis and the experimental result have also proven the merit of CGDSPT.

关键词： Clustering algorithms

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