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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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Recognizing Cursive Arabic Text:Using statistical features and interconnected mono-HMMs

Recognizing Cursive Arabic Text:Using statistical features a...

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2011 4th International Congress on image and Signal processing(第四届图像与信号处理国际学术会议 CISP 2011)

作者： M S Khorsheed H Al-Omari Image Processing and Signal Analysis & Recognition (IPSAR) Research Group Computer Research Institute King Abdulaziz City for Science and Technology PO Box 6086 Riyadh 11442 Saudi Arabia

This paper presents a cursive Arabic text recognition system. The system decomposes the document image into test line images and extracts a set of simple statistical features from a one-pixel width window which is sliding a cross that text line. It then injects the resulting feature vectors to Hidden Markov Models. The proposed system is applied to a data corpus which includes Arabic text of more than 600 A4-size sheets typewritten in multiple computergenerated fonts.

关键词： Document analysis pattern recognition Arabic text recognition hidden Markov models HTK

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A global method for reducing multidimensional size graphs

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6658 LNCS卷 1-11页

作者： Cerri, Andrea Frosini, Patrizio Kropatsch, Walter G. Landi, Claudia Vienna University of Technology Faculty of Informatics Pattern Recognition and Image Processing Group Austria Università di Bologna ARCES Dipartimento di Matematica Italy Università di Modena e Reggio Emilia Dipartimento di Scienze e Metodi dell'Ingegneria Italy

ISBN: (纸本)9783642208430

This paper introduces the concept of discrete multidimensional size function, a mathematical tool studying the so-called size graphs. These graphs constitutes an ingredient of Size Theory, a geometrical/topological approach to shape analysis and comparison. A global method for reducing size graphs is presented, together with a theorem stating that size graphs reduced in such a way preserve all the information in terms of multidimensional size functions. This approach can lead to simplify the effective computation of discrete multidimensional size functions, as shown by examples. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Graphic methods

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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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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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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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Higher order MRF for foreground-background separation in multi-spectral images of historical manuscripts 10

Higher order MRF for foreground-background separation in mul...

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2010 IAPR Workshop on Document Analysis Systems, DAS 2010

作者： Lettner, Martin Sablatnig, Robert Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology 1040 Vienna Austria

ISBN: (纸本)9781605587738

Multi-spectral imaging for the analysis and preservation of ancient documents has gained high attention in recent years. While readability enhancement is based on the multi-spectral image corpus, foreground-background separation still relies mainly on gray level or color images. In this paper we propose a foreground-background separation algorithm designed for multi-spectral images. The main contribution is the simultaneously utilization of spectral and spatial features. While spectral features incorporate the spectral components of the multi-spectral images, the spatial features are based on stroke properties. Higher order Markov Random Fields enables an efficient way to combine both features. To solve higher order energy functions, we introduce a new message update rule in the well known belief propagation algorithm based on a higher order potential function. Copyright 2010 ACM.

关键词： image enhancement

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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

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

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