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IEEE International Conference on Mechatronics and Automation

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan China College of Mechanical Engineering TaiYuan University of Technology TaiYuan China Institute for Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology WuHan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： Image edge detection Image restoration Degradation Atmospheric modeling Optical scattering Optical imaging

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Study on the Method of Image Restoration in the Environment of Dust

Study on the Method of Image Restoration in the Environment ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： Image edge detection Image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

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A close-up detection method for movies

A close-up detection method for movies

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IEEE International Conference on Image Processing

作者： Huiying Liu Min Xu Qingming Huang Jesse S. Jin Shuqiang Jiang Changsheng Xu Chinese Academy of Sciences Beijing China Key Laboratory of Intelligence Information Process. Institute of Computer Technology Chinese Academy and Sciences Beijing China Faculty of Engineering and Information Technology Sydney University of Technology Sydney Australia School of Design Communication and IT University of Newcastle Australia National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Close-up (CU) is a photographic technique which tightly frames a person or an object. In movies, it is applied to guide audience attention and to evoke audience emotion. In this paper, we detect face CU, object CU, and lean of movies, which are widely used to romance emotions. A lean consists of shots in a sequence, with a close-up shot as focus. A set of features are extracted by considering movie making techniques and human attention for CU detection. The features are average saliency, color entropy, color variance, face height, skin area, and texture scales. These features are tested through statistical hypothesis test to be significantly discriminating for CUs. Then, Support Vector Machine (SVM) is applied on these features to detect face CU and object CU. Based on the face CU and object CU detection result, lean is further detected by investigating the changing of the face/object size. Lean detection is of challenge due to the technique of montage. We solve this problem through color similarity estimation and SIFT point matching. Experimental results on four full length movies verify the effectiveness of the proposed method.

关键词： Copper Face Motion pictures Feature extraction Color Skin Humans

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N-order difference heuristic model of fuzzy time series forecasting

N-order difference heuristic model of fuzzy time series fore...

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IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS)

作者： Kai Chi Wen-Gang Che Pattern Recognition and Intelligent System School of Information Engineering and Automation Kunming University of Science and Technology China Key Laboratory of Computer Technology and Application of Yunnan Province Kunming University of Science and Technology China

Fuzzy time series forecasting model is an effective method to solve the nonlinear problems forecasting. However, most published fuzzy time series based models did not count the change trend implicit in historical datum. In this paper, authors proposed a novel method which applied heuristic information to the fuzzy time series model based on Fibonacci sequence. As an example, the USD/JPY exchange rate is tested in this model. The results show that this method not only improves the forecasting accuracy, but decreases the computational complexity.

关键词： Predictive models Technology forecasting Economic forecasting Uncertainty Fuzzy systems pattern recognition Intelligent systems Automation Channel hot electron injection Laboratories

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Adaptive kernel principal analysis for online feature extraction

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World Academy of Science, Engineering and Technology 2009年 59卷 288-293页

作者： Ding, Mingtao Tian, Zheng Xu, Haixia Department of Science Northwestern Polytechnical University Xi'an China Department of Science Department of Computer Science and Engineering Northwestern Polytechnical University Xi'an China National Key Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard *** formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.

关键词： Covariance matrix

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Palmprint recognition using kernel PCA of Gabor features

Palmprint recognition using kernel PCA of Gabor features

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International International Symposium on computer and Information Sciences (ISCIS)

作者： Murat Ekinci Murat Aykut Computer Vision and Pattern Recognition Laboratory Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

This paper presents a new method for automatic palmprint recognition based on kernel PCA method by integrating the Gabor wavelet representation of palm images. Gabor wavelets are first applied to derive desirable palmprint features. The Gabor transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can produce salient features that are most suitable for palmprint recognition. The kernel PCA method then nonlinearly maps the Gabor-wavelet image into a high-dimensional feature space. The proposed algorithm has been successfully tested on two different public data sets from the PolyU palmprint databases for which the samples were collected in two different sessions.

关键词： Kernel Principal component analysis Image recognition Testing Biometrics Face recognition Lighting pattern recognition computer vision Image databases

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Study of software fault tolerance system evaluation strategy

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Wuhan Daxue Xuebao (Xinxi Kexue Ban)/ Geomatics and Information Science of Wuhan University 2008年第10期33卷 1018-1021页

作者： Liu, Xin Yin, Qian Guo, Ping Lab. of Pattern Recognition and Intelligent System Beijing Normal University 19 Xinjiekou Beijing 100875 China State Key Laboratory of Computer Science Institute of Software CAS 4 Zhongguancun Beijing 100080 China

Software fault tolerance technique is used to ensure high reliability. In order to evaluate the quality of fault-tolerant systems, expected execution time and cost must be considered. A new evaluation strategy is proposed to evaluate, evaluating the system with three groups of quality factors, which is the capability, the time and the cost. By using them, the system's integrative quality factor can be calculated. When applying this strategy, we compare the quality of software systems which is consist of different fault-tolerant components. The results can be used to improve fault-tolerant system. To achieve the required optimal result, several optimization schemes are given to meet different project demands.

关键词： Quality control

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Gene Expression Clustering: a Novel Graph Partitioning Approach

Gene Expression Clustering: a Novel Graph Partitioning Appro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yanhua Chen Ming Dong Manjeet Rege Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the clustering problem using a graph theoretical approach, and apply a novel graph partitioning model -isoperimetric graph partitioning (IGP), to group biological samples from gene expression data. The IGP algorithm has several advantages compared to the well-established spectral graph partitioning (SGP) model. First, IGP requires a simple solution to a sparse system of linear equations instead of the eigen-problem in the SGP model. Second, IGP avoids degenerate cases produced by spectral approach to achieve a partition with higher accuracy. Moreover, we integrate unsupervised gene selection into the proposed approach through two-way ordering of gene expression data, such that we can eliminate irrelevant or redundant genes in the data and obtain an improved clustering result. We evaluate our approach on several well-known problems involving gene expression profiles of colon cancer and leukemia subtypes. Our experiment results demonstrate that IGP constantly outperforms SGP and produces a better result that is closer to the original labeling of sample sets provided by domain experts. Furthermore, the clustering accuracy is improved significantly when IGP is integrated with the unsupervised gene (feature) selection.

关键词： Gene expression Biological system modeling Diseases Biological systems Data analysis Performance analysis Clustering algorithms Partitioning algorithms Equations Colon

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Incorporating User Provided Constraints into Document Clustering

Incorporating User Provided Constraints into Document Cluste...

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IEEE International Conference on Data Mining (ICDM)

作者： Yanhua Chen Manjeet Rege Ming Dong Jing Hua Department of Computer Science Machine Vision and Pattern Recognition Laboratory Detroit MI USA Department of Computer Science Graphics and Imaging Laboratory Detroit MI USA

Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.

关键词： Iterative algorithms Clustering algorithms Symmetric matrices Data mining Databases Clustering methods Couplings Machine vision pattern recognition computer graphics

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Localized Support Vector Machines for Classification

Localized Support Vector Machines for Classification

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International Joint Conference on Neural Networks (IJCNN)

作者： Ming Dong Jing Wu Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in each category that are nearest to the given input pattern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM.

关键词： Support vector machines Support vector machine classification Kernel USA Councils Solids Machine learning Training data Nails Neural networks Clustering algorithms

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