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Retinal Vessel Tracking Using Bilateral Filter Based on Canny Method

Retinal Vessel Tracking Using Bilateral Filter Based on Cann...

2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

作者： Chuang Miao Xiaolin Sun Zhenhua Chai Yonghang Jiang Samuel H. Chang Zhao Y. Duan Ling G. Wang College of Electronic Science & Engineering Jilin University Changchun China Institute of Mathematics Jilin University Changchun China College of Computer Software Jilin University Changchun China

ISBN: (纸本)9781424458653

This paper presents a new method to measure retinal blood vessel with sub-pixel accuracy. The method is based on a modified Canny edge detection method with a bilateral filter. There are three stages of the method. First, the bilateral filter is used to remove the vessel background noises, and then the Canny detector is used to detect all vessel edges. Second, vessel sample profiles crossed vessel boundaries are obtain based on Canny edges. Third, the new vessel positions are measured from the Gaussian fits of the sample profiles. The validation of the method is tested by real images with outputs of the vessel walls and center lines in sub-pixel accuracy.

关键词： bilateral filters Blood Vessels pressure vessels Retinal Vessels 3-glucosaminoglycan sulfate blood vessel wall BACKGROUND NOISE

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Vulnerability assessment of distribution networks based on multi-agent system and quantum computing

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ICIC Express Letters 2010年第5 B期4卷 1761-1766页

作者： Meng, Xiangping Yan, Kaige Tan, Jingweijia Pian, Zhaoyu School of Electrical Engineering and Information Technology Changchun Institute of Technology Changchun 130012 China College of Computer Science and Technology Jilin University Changchun 130012 China

To overcome the defects of information architecture of distribution networks, a coordination of distributed control structure is presented based on multi-agent system for vulnerability assessment. The new architecture makes use of multi-agent system's advantages of handling of complex, dynamic, and distributed problems. Meanwhile, since the classical reinforcement learning of multi-agent system would be failed, when the number of agents is too large, a quantum reinforcement learning algorithm is proposed by combining quantum computing to perfect the assessment. Finally, the numerical simulations for both the classical Nash Q-learning and quantum reinforcement learning, and vulnerability assessment experiments based on an actual distribution network, have been investigated respectively. The results of simulation demonstrate that the improved model is more precise and highly efficient for the vulnerability assessment. © 2010 ICIC International.

关键词： computer simulation

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Notice of Retraction: C++ Teaching Guided by System Theory and Practice Theory in Philosophy

Notice of Retraction: C++ Teaching Guided by System Theory a...

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International Workshop on Education Technology and computer science, ETCS

作者： Yu Helong Zhao Yuxin Chen Guifen Jiao Hongbin College of Information Technology Jilin Agricultural University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Environmental Engineering Jilin Architectural and Civil Engineering Institute Changchun China

This article has been retracted by the publisher.

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A hybrid algorithm of minimum spanning tree and nearest neighbor for classifying human cancers

A hybrid algorithm of minimum spanning tree and nearest neig...

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2010 3rd International Conference on Advanced computer Theory and engineering, ICACTE 2010

作者： Zhou, Chunbao Wan, Liming Liang, Yanchun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

ISBN: (纸本)9781424465408

Classification and prediction of different cancers based on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighbor algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighbor algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighbors for k-NN. Firstly we build a minimum spanning tree (MST) based on Euclidean distance between each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighbors for unknown class sample until all the samples are expended or the class for unknown sample is obtained. This hybrid algorithm is referred to as MSTNN. The hybrid algorithm MSTNN is compared with kNN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 binary class gene expression datasets and 3 multi-class gene expression datasets: Leukemial, Leukemia2, and Leukemia3 *** human cancers. The MSTNN algorithm improves 5.65% better than k-NN on average LOOCV accuracy and 13.80% better than kNN on testing datasets classification average accuracy, and achieves the best performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasible to classify human cancers. © 2010 IEEE.

关键词： Classification (of information)

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A mutual information and information entropy pair based feature selection method in text classification

A mutual information and information entropy pair based feat...

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2010 International Conference on computer Application and System Modeling, ICCASM 2010

作者： Pei, Zhili Zhou, Yuxin Wang, Lihua Liu, Lisha Lu, Yinan Kong, Ying College of Computer Science and Technology Inner Mongolia University for Nationalities Tongliao China Mathematics School and Institute Jilin University Changchun China College of Mechanical Engineering Inner Mongolia University for Nationalities Tongliao China Command Automation WorkStation No. 65367 of PLA Tonghua China College of Computer Science and Technology Jilin University Changchun China College of Basic Medical Dalian Medical University Dalian China

ISBN: (纸本)9781424472369

Text classification is an important research field of data mining topics. This article brings a mutual information and information entropy pair based feature selection method (MIIEP FS) based on the theory of information entropy and information entropy pair concept. This method measure the classification effect using feature by mutual information method and show the difference extent between the features being selected and the ones selected by information entropy. The experimental results show that the MIIEP-FS method proposed is more effective than MI and CHI methods. Macro F1 degrees of different kinds of machine learning algorithms: Naive Bayes and KNN method are higher by MIIEP-FS method, sometimes even more than the ones of support vector machines. © 2010 IEEE.

关键词： Feature Selection

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A K-Nearest Neighbor Algorithm Based on Cluster in Text Classification

A K-Nearest Neighbor Algorithm Based on Cluster in Text Clas...

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2010 International Conference on computer,Mechatronics,Control and Electronic engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Chun-Yan WANG Jian-Gang Li Kuo Zhang Yu-Guang YAN Department of computer science and technology Changchun Normal College Changchun China Institute of mechanical science and engineering Jilin University Changchun China Department of computer science and technology Changchun Normal College Changchun China

The K-Nearest Neighbor Algorithm (K-NN) is an important approach for automatic text classification. In this paper, cluster was applied In order to overcome the disadvantages of the traditional K-NN algorithm. First Clustering was utilized in training set through an improved K-mean approach to select the most representative samples as cluster *** we compute the comparability between the testing samples and the central vector of each cluster. A K-NN algorithm based on cluster was presented ,The experiment results verify that this classification algorithm is much faster than the traditional K-NN algorithm, and it can raise the accuracy.

关键词： text classification k-Nearest Neighbor cluster

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Research on Chinese-Japanese sign language translation system

Research on Chinese-Japanese sign language translation syste...

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International Conference on Frontier of computer science and Technology

作者： Liu, Zhen Zhang, Xiaomei Kato, Jien Graduate School of Engineering Nagasaki Institute of Applied Science Japan 536 Aba-machi Nagasaki 851-0193 Japan Special Education College Changchun University China Graduate School of Information Science Nagoya University Japan College of Computer Science and Technology Jilin University China

ISBN: (纸本)9780769541396

Sign Language is a main way for deaf to express their thought and emotions, obtain information and participate in social activities. All sign languages of the world are not very similar each other. With the increasing of economic and cultural exchanges between China and Japan, more and more deaf from the two countries need to learn partner's sign language. At the same time, the translation of ChineseJapanese sign languages has the important means right now. In this paper, the features of the two sign languages, Chinese sign language and Japanese sign language, are studied and compared. And, a scheme of Chinese-Japanese sign language translation system is proposed. © 2010 IEEE.

关键词： Chinese Japanese sign language deaf aid handicapped aids language translation language translation natural language processing

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Automatic classification of Deep Web databases based on centroid and WordNet

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Journal of Computational Information Systems 2010年第1期6卷 63-70页

作者： Zuo, Wanli Wang, Ying Wang, Xin Zhang, Dandan Peng, Tao College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Computation and Knowledge Engineering China College of Software Changchun institute of technology Changchun 130012 China

Deep Web contains a significant amount of visited information, in order to be able to make full use of the information, we need to organize it according to different domain. Therefore, it is imperative that Deep Web databases should be classified by domain automatically. In this paper, a new Deep Web database classification framework is proposed, which adds semantic information to feature vectors and centroid vector by extracting the synsets of terms which can be obtained from WordNet, and replace the terms by corresponding synsets in the feature vectors and centroid vector to achieve dimensionality reduction of vectors. Lastly, highlight the semantic feature vectors by semantic centroid vector, and classify the highlighted semantic feature vectors by classification algorithm. Experiments show that experiment 3 which combines experiment 1 and experiment 2 can effectively improve the classification accuracy of Deep Web databases. Copyright © 2010 Binary Information Press.

关键词： Vectors

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A New Fuzzy Adaptive Multi-Population Genetic Algorithm Based Spam Filtering Method

A New Fuzzy Adaptive Multi-Population Genetic Algorithm Base...

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International Conference on Information engineering and computer science (ICIECS)

作者： Gang Wang Yuan-ning Liu Xiao-dong Zhu Hui-ling Chen Zhen Liu College of Computer Science and Technology Jilin University Changchun China Graduate School of Engineering Nagasaki Institute of Applied Science Japan

Internet e-mails have become a common medium of communication for nearly every one. With the fast growing, spam interferes with valid email, and bothers users. This paper proposes a new fuzzy adaptive multi-population genetic algorithm (FAMGA), in order to automatically find the best feature subset to classify spam e-mails. FAMGA consists of multiple subpopulations, and each population runs independently. We design two fuzzy controllers to adjust the crossover rate and the size of each subpopulation, in order to prevent premature convergence of the population. Two publicly available benchmark corpora for spam filtering, the PU1 and Ling-Spam, are used in our experiments. The results of experiments show that the proposed method improves the performance of spam filtering, and is better than other methods of feature selection.

关键词： Classification algorithms Filtering Support vector machines Indexes Artificial neural networks Unsolicited electronic mail

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Research on digital campus of higher colleges and its management platform

Research on digital campus of higher colleges and its manage...

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International Conference on Educational and Information Technology (ICEIT)

作者： Ming Li Limin Wang Xuming Han Jianhua Jiang School of Information Jilin University of Finance and Economics Changchun China College of Computer Science and Technology Jilin University Changchun China Institute of Information and Spreading Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9781424480333

Currently almost all universities have the following problems in the information construction process, such as lack of uniform development programming, lack of share effectively for information, applications lack of effective integration, user lack of unified interface, etc. In order to implement the objective of lean management, use distributed resources stored in different domains, improve the quality and efficiency of management, achieve the information interconnection and integration among different systems in higher education, we adopt some technologies, such as SOA (Service-Oriented architecture) software framework, SOA data transformation technique, mode integration technique, web service discovery technique in campus, web service semantic descriptive technique, integration mapping technique, search engine application technique and so on, so the data management platform, named as URP(University Resource Planning) is established in this paper. It could improve effectively the level of management, the quality of teaching and management effectiveness.

关键词： Service oriented architecture Multimedia communication computer architecture

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