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image enhancement based on improved fractional differentiation

Journal of Computational Information Systems 2011年第1期7卷 257-264页

作者： Gao, Chaobang Zhou, Jiliu Zheng, Xiuqing Lang, Fangnian College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China School of Computer Science Sichuan University Chengdu 610064 China

In this paper, according to the development of the fractional differentiation and its applications in the modern signal processing, we improve the numerical calculation of fractional differentiation by piecewise quadratic interpolation equation, that is, improved fractional differentiation, IFD for short, and propose a new corresponding operator, IFD operator. And we apply this new operator to image enhancement. Experiments show that, for texture-rich digital image, the capability of nonlinearly enhancing comprehensive texture details by IFD is very obvious. For quantity analysis, we particularly calculate the information entropy and average gradient. Comparing with fractional differentiation, the image obtained by IFD is clearer. © 2011 Binary Information Press.

关键词： image enhancement

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Adaptive dynamic loading and unloading mechanism applied to development environment for image processing algorithm

Adaptive dynamic loading and unloading mechanism applied to ...

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International Conference on Computer Science and Service System (CSSS)

作者： Haifeng Lu Tianxu Zhang Wei Yan Zhao Cheng State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Department of Technology Hubei Daily Media Group Wuhan China

An advanced development environment contributes to research process greatly. This paper presents a novel adaptive dynamic loading and unloading mechanism applied to development environment for image processing algorithm and two implementation methods of the mechanism. Based on the inherent characteristic of variable arguments of functions in programming language and explicit linking technique for dynamic link library (DLL), the mechanism is to integrate the image processing algorithm into the DLL file which is independent from the development environment, and to make the development environment identify the normative algorithm DLL and load functions of image processing algorithm successfully when needed. The proposed mechanism has been implemented in a development environment written by C/C++ language. The results show that the development environment based on the novel mechanism can improve work efficiency by making researchers only focus on the development of image processing algorithm rather than the other inefficient and repetitive work. At the same time the standardability, reusability and confidentiality of image processing algorithm can be promoted greatly. This mechanism is also applicable to other similar systems.

关键词： image processing Heuristic algorithms Algorithm design and analysis Loading Computational modeling Computer languages Mathematical model

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Estimating the Connection Probability for 2-D Mesh Networks by Monte Carlo Methods

Estimating the Connection Probability for 2-D Mesh Networks ...

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International Conference on Computer and Management (CAMAN)

作者： Meilian Liang Binmei Liang Zehui Shao Xiaodong Xu School of Mathematics and Information Science Guangxi University Nanning China Key Laboratory of Pattern Recognition and Intelligent Information Processing School of Information Science & Technology University of Chengdu Chengdu China Guangxi Academy of Sciences Nanning China

Fault tolerance is a central issue in the design and implementation of interconnection networks for large parallel systems. Connection probability of a network is a good network fault tolerance measure. For a mesh of given size and node failure probability, the gap between the known upper and lower bounds on the connection probability is often very large. In this paper we design algorithms to estimate the connection probability for 2-D meshes by Monte Carlo methods. The experiment is carefully designed and performed, and the simulation results give good estimates of the connection probability for 2-D meshes and can be used to evaluate the known lower and upper bounds on connection probability for 2-D meshes.

关键词： Mesh networks Fault tolerance Fault tolerant systems Upper bound Monte Carlo methods Computers Simulation

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Adaptive dynamic loading and unloading mechanism applied to development environment for image processing algorithm

Adaptive dynamic loading and unloading mechanism applied to ...

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2011 International Conference on Computer Science and Service System, CSSS 2011

作者： Lu, Haifeng Zhang, Tianxu Yan, Wei Cheng, Zhao State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Technology Hubei Daily Media Group Wuhan 430077 China

ISBN: (纸本)9781424497638

关键词： Reusability

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Video vehicle detection through multiple background-based features and statistical learning

Video vehicle detection through multiple background-based fe...

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International Conference on Intelligent Transportation

作者： Mingxia Sun Kunfeng Wang Ming Tang Fei-Yue Wang Jinfeng Yang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences (CAS) Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences (CAS) Beijing China

This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term.

关键词： Vehicles Feature extraction Training image edge detection Vehicle detection Meteorology Radio frequency

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Pulsar/CNS integrated navigation based on federated UKF

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Journal of Systems Engineering and Electronics 2010年第4期21卷 675-681页

作者： Jin Liu Jie Ma Jinwen Tian State Key Laboratory for Multi-spectral Information Processing Technologies Institue for Pattern Recognition and artifical Intelligence Huazhong University of Science and Technology Wuhan 430074 P. R. China

In order to improve the autonomous navigation capability of satellite,a pulsar/CNS（celestial navigation system） integrated navigation method based on federated unscented Kalman filter（UKF） is *** celestial navigation is a mature and stable navigation ***,its position determination performance is not satisfied due to the low accuracy of horizon *** pulsar navigation is a new navigation method,which can provide highly accurate range *** major drawback of single pulsar navigation is that the system is completely *** two methods are complementary to each other,the federated UKF is used here for fusing the navigation data from single pulsar navigation and *** to the traditional celestial navigation method and single pulsar navigation,the integrated navigation method can provide better navigation *** simulation results demonstrate the feasibility and effectiveness of the navigation method.

关键词： autonomous navigation celestial navigation system（CNS） pulsar federated unscented Kalman filter（UKF）.

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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IEEE International Conference on Systems Biology

作者： Mingjun Wang Hong-Bin Shen Tatsuya Akutsu Jiangning Song State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences (CAS) Tianjin China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC Australia

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations.

关键词： Support vector machines Accuracy Proteins Feature extraction Humans Amino acids Databases

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An accurately located method and secure scheme for image authentication

An accurately located method and secure scheme for image aut...

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International Conference on Multimedia Technology

作者： Wang, Zuxi Wang, Wenzong Dong, Liefeng Ye, Peng Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China Ningbo Polytechnic Ningbo China

ISBN: (纸本)9781424478743

A novel authentication watermarking scheme for images is proposed in this paper, which holds accuracy location and high security at the same time. In the scheme, different keys are selected for different host data, and the generation and insertion of watermark is dependent on the selected key. Analyzing different pieces of watermarked data in order to launch an attack becomes impossible because of the lack of common characteristics in these pieces. Beside, to increase the security of system, the chaos technology is used to encrypt the watermark and shuffle the location of embedding. A method for tamper detecting based on the running window is proposed in the scheme. Not only can it distinguish malicious attacks from unintentional attacks, but also it can locate the tampered area accurately. Experimental results show this semi-fragile watermarking algorithm is effective and practical for content authentication. ©2010 IEEE.

关键词： Authentication

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424481354

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved. © 2010 IEEE.

关键词： Support vector machines

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

作者： Jiang, Ziheng He, Jing Guo, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9781424481354

In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique. © 2010 IEEE.

关键词： Support vector machines

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