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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Wang, Mingjun Shen, Hong-Bin Akutsu, Tatsuya Song, Jiangning State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC 3800 Australia

ISBN: (纸本)9781457716669

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. © 2011 IEEE.

关键词： Amino acids

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Comparison of Several Traffic Forecasting Methods Based on Travel Time Index Data on Weekends

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Journal of Shanghai Jiaotong university(Science) 2010年第2期15卷 188-193页

作者：张扬史文欢刘允才 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University

Traffic forecasting provides the estimation of future traffic state to help traffic control,travel guide,etc. This paper compared several widely used traffic forecasting methods,and analyzed each one's performance in detail to make conclusions,which could redound to researchers choosing an appropriate traffic forecasting method in their own works. Compared with conventional works,this paper creatively assessed the performance of traffic forecasting methods based on travel time index (TTI) data prediction,which made the accuracy of our comparison better.

关键词： traffic forecasting travel time index (TTI) performance evaluation

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Sampling step importance in hierarchical semantic segmentation of microscopic images

Sampling step importance in hierarchical semantic segmentati...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Cristian Smochina Vasile Manta Walter Kropatsch Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iasi Romania Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Austria

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we improve the technique presented in [17] used to identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken from colon tissues. In the proposed enhanced approach, the crypt inner boundary is detected using the closing morphological pyramid instead of morphological hierarchy. The outer crypt border is determined by the epithelial nuclei, overlapped by the maximal isoline of the inner boundary. The use of sampling in building the pyramid offers computational efficiency, reduces the amount of used memory, increase the robustness and preserve the quality results. An analysis of the two approaches is performed considering the number of pixels processed to create each level. Also the relation between the levels of the hierarchical structures is established.

关键词： Cryptography image segmentation image reconstruction image resolution Microscopy Buildings Shape

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Unusual Event Detection and Prediction in Real-life Scenes

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Journal of Shanghai Jiaotong university(Science) 2010年第1期15卷 19-23页

作者：张一杨杰 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University

In this paper,we consider unusual event detection problem in a novel viewpoint and provide an algorithm to solve the *** actions or events in the scene is usual or not will eventually be reflected on the changes of some basic *** summarize these basic event features and propose special representation for each of *** we can model these features in a uniform mode using adaptive Gaussian mixture *** and unsupervised unusual event detection algorithm can be designed to fit various situations based on this *** superiority of our model is that it can detect unusual event automatically without to know the determinate model of unusual *** conclusion,we provide two applications to verify the effectiveness of our model.

关键词： unusual event detection adaptive Gaussian mixture model linear discriminant analysis hidden Markov model trajectory distance metric

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Multi-temporal Satellite images Change Detection Algorithm Based on NSCT

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Procedia Engineering 2011年 24卷 252-256页

作者： Wei Cui Zhenhong Jia Xizhong Qin Jie Yang Yingjie Hu College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

In order to get the change detection *** unsupervised change detection algorithm for multi-temporal satellite image based on NSCT (non-subsampling contourlet transform) and k-means clustering is proposed in this paper. For each pixel in the log-ratio image, multi-scale and multi-direction feature vector is extracted by NSCT and the reconstruction of the log-ratio image is obtained. The threshold is produced by using the k-means clustering algorithm and can distinguish between the unchanged and the change region. Finally, the change detection map is achieved. Some satellite images are used to verify the proposed method and the results shows that it has a higher stability and accuracy against Gaussian and speckle noise than traditional algorithms.

关键词： NSCT k-means clustering multi-temporal satellite images change detection

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NSCT Remote Sensing image Denoising Based on Threshold of Free Distributed FDR

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Procedia Engineering 2011年 24卷 616-620页

作者： Jihong Liu Zhenhong Jia Xizhong Qin Jie Yang Yingjie Hu College of Information Science and Engineering Xinjiang University Urumuqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn’t depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remote sensing image; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details’ information of the image, thus obviously ameliorate the visual effect of the image.

关键词： image denoising False Discovery Rate Non-subsmapled contourlet transform Soft threshold Shift invariance

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A New Technique for Multispectral and Panchromatic image Fusion

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Procedia Engineering 2011年 24卷 182-186页

作者： Qifan Wang Zhenhong Jia Xizhong Qin Jie Yang Yingjie Hu College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance.

关键词： image fusion fourth-order correlation coefficient (FOCC) multispectral remote sensing nonsubsampled contourlet transform (NSCT) relative entropy

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An Algorithm for Remote Sensing image Denoising Based on the Combination of the Improved BiShrink and DTCWT

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Procedia Engineering 2011年 24卷 470-474页

作者： Minghui Li Zhenhong Jia Jie Yang Yingjie Hu Dianjun Li College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

By considering the strong correlation between wavelet coefficients of the actual image, while bivariate model is only a statistical model for the interscale dependency of wavelet coefficient with parent coefficient, without taking into account the correlation of adjacent coefficient. Therefore, based on the shift-invariance and better directionality of the dual-tree complex wavelet transfer (DTCWT) and incorporating neighboring wavelet coefficients with BiShrink, a novel BiShrink threshold and DTCWT remote sensing image denoising method is presented. Experimental results show the proposed algorithm gets better PSNR than other methods mentioned observably. In terms of visual quality the proposed algorithm can get the images with more details smooth profiles and aliasing is restricted

关键词： Dual Tree Complex Wavelet Transfer(DTCWT) BiShrink threshold Neighboring coefficients Remote sensing image denoising

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An Adaptive Mean Shift Algorithm Based on LSH

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Procedia Engineering 2011年 23卷 265-269页

作者： Yanbin Cui Kui Cao Gang Zheng Fan Zhang College of Computer and Information Engineering Henan University Kaifeng Henan 475001 PR China Institute of Image Processing and Pattern Recognition Henan University Kaifeng Henan 475001 PR China Kaifeng Electric Power Company Kaifeng 475001 PR China

The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An approximate neighborhood queries method is presented for the computation of high dimensional data, in which, the locality-sensitive hashing (LSH) is used to reduce the computational complexity of the adaptive mean shift algorithm. Experimental results show that the proposed algorithm can reduce the complexity of the adaptive mean shift algorithm and can produce a more accurate classification than the fixed bandwidth mean shift algorithm.

关键词： pattern Classification Adaptive bandwidth Adaptive mean-shift Locality-sensitive hashing

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Probabilistic Model-Based Silhouette Refinement for Gait recognition

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Journal of Shanghai Jiaotong university(Science) 2010年第1期15卷 24-30页

作者：张元元吴晓娟阮秋琦 Institute of Image Processing and Pattern Recognition Shandong University Institute of Information Science Beijing Jiaotong University

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is *** silhouettes are sequentially refined in two levels according to two different probabilistic *** first level is within-sequence *** silhouette in a particular sequence is refined by an individual model trained by the gait images from current *** second level is between-sequence *** the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of *** intention is to preserve the within-class similarity and to decrease the interaction between one class and *** experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.

关键词： silhouette refinement probabilistic model gait recognition performance evaluation

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