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Mutual Information Optimization Based Dynamic Log-Polar Image Registration

Journal of Shanghai Jiaotong university(Science) 2015年第1期20卷 61-67页

作者：张葵张晓龙徐新付晓薇 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan University of Science and Technology

Log-polar transformation(LPT)is widely used in image registration due to its scale and rotation invariant *** LPT,rotation and scale transformation can be made into translation displacement in log-polar coordinates,and phase correlation technique can be used to get the *** LPT based image registration,constant samples in digitalization processing produce less precise and effective ***,dynamic log-polar transformation(DLPT)is used in this *** is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical ***,DLPT can get more precise and effective transformation results than the conventional *** information(MI)is a similarity measure to align two images and has been used in image registration for a long *** optimal transform for image registration can be obtained by maximizing MI between the two *** registration based on MI is robust in noisy,occlusion and illumination changing *** this paper,we study image registration using MI and *** with digitalizing images and with real image datasets are performed,and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.

关键词： image registration mutual information(MI) log-polar transformation(LPT) phase correlation technique

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Consensus algorithms for biased labeling in crowdsourcing

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Information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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A vascular bifurcations detection method based on Transfer Learning model

A vascular bifurcations detection method based on Transfer L...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiaoming Liu Jia Wang Zhou Yang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China College of Computer Science and Technology Wuhan University of Science and technology Wuhan China

ISBN: (纸本)9781509037117

Many distinguished methods for vascular network detection in fundus images were proposed to help the diagnosis of clinical diseases. The vascular bifurcation sample in OCT projection images is quite limited while it is sufficient in the corresponding fundus images. In this paper, we proposed a transfer learning-based method to detect the vascular bifurcations in OCT projection images using supervised transfer learning method. The samples from fundus images are utilized with transfer learning technique for vascular bifurcations detection in OCT projection images. The experimental results show the accuracy of vascular bifurcations detection can be improved by the proposed method.

关键词： Bifurcation Gabor filters Training Support vector machines Retina Learning systems Optical filters

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An Online Parameter Estimation Method for a class of Nonlinear Systems Based on Particle Filter 34

An Online Parameter Estimation Method for a class of Nonline...

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第三十四届中国控制会议

作者： XIE Guo ZHANG Dan HEI Xinhong QIAN Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781467374439

Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.

关键词： Nonlinear system,Particle filter,Parameter set,Online parameter identification

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A multi-objective optimization algorithm based on tissue P system for VRPTW 11th

A multi-objective optimization algorithm based on tissue P s...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Dong, Wenbo Zhou, Kang Qi, Huaqing He, Cheng Zhang, Jun Song, Bosheng School of Math and Computer Wuhan Polytechnic University WuhanHubei430023 China Department of Economics and Management Wuhan Polytechnic University WuhanHubei430023 China Key Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology WuhanHubei430074 China School of Automation Huazhong University of Science and Technology WuhanHubei430074 China

ISBN: (纸本)9789811036132

Vehicle routing problem with time windows has an important practical significance, but it is NP-Hard problem. In order to solve the problem, an optimization algorithm based on P system is proposed. The encoding of glowworm’s location is considered as evolutionary object and discrete glowworm evolution mechanism and variable neighborhood evolution mechanism are used as sub-algorithms. In this paper, the motion equations and related motion rules of glowworm algorithm are improved to optimize the performance of the algorithm. Meanwhile, in order to enlarge the search area of solution space and improve the precision, the variable neighborhood evolution mechanism is redesigned. Cell communication rules are used to exchange information between cells. Moreover, this paper introduced the concept of Pareto dominance to evaluate the advantages and disadvantages of the object, as a result, this method returns not a single non-dominated solution but a set of no-dominated solutions. At last, by solving the different Solomon numerical examples and simulation results show that the algorithm is easier to jump out of local optimal both achieves very good results in the number of vehicles and distance cost, besides, generates a lot of new solutions which are different from the database. This algorithm has the features of faster convergence rate and accurate precision, and it is competitive with other heuristic or metaheuristic algorithms in the literature. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Multiobjective optimization

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Connected dominating set construction algorithm for wireless networks based on connected subset

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Journal of Communications 2016年第1期11卷 50-57页

作者： Tang, Qiang Luo, Yuan-Sheng Xie, Ming-Zhong Li, Ping Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

In this paper, a Connected Dominating Set (CDS) construction algorithm CSCDS (Connected Subset based CDS) is proposed, which is based on the connected subset concept. The CSCDS contains two main stages, which are dominating set construction stage and connected dominating set construction stage respectively. In the first stage, the dominators are proposed based on the one hop white neighbor information, and the redundant dominators are reduce to obtain the minimum number of dominators. In the second stage, the CDS is constructed based on the connected subset, which is used as the basis to select the connectors. The message complexity is O(Δ2) (O(Δ2) is a linear relationship function with Δ2, and Δ is the maximum one hop neighbor degree), Simulation results show that CSCDS has smaller size compared with the classical algorithms. © 2016 Journal of Communications.

关键词： Wireless networks

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Hyperspectral unmixing based on iteratively re-weighted smoothed l0 regularisation

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International Journal of Wireless and Mobile Computing 2016年第3期11卷 228-237页

作者： Li, Yan Deng, Chengzhi Wang, Shengqian Wang, Hui School of Information Engineering Nanchang Institute of Technology Jiangxi Nanchang330099 China Jiangxi Province Key Laboratory Water Information Cooperative Sensing and Intelligent Processing Jiangxi Nanchang330099 China

Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. Sparse-based approach has recently received much attention in hyperspectral unmixing area. Sparse unmixing is based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combination of a number of pure spectral signatures that are known in advance. Despite the success of sparse unmixing based on the L0, L1/2 or L1 regulariser, the limitation of this approach on its computational complexity or sparsity affects the efficiency or accuracy. In this paper, as iteratively re-weighted algorithm can help to find the much sparser solution in optimal problems, we proposed a sparse unmixing method based on iteratively re-weighted smoothed L0 regularisation, named RSL0SU. Then a variable splitting and augmented Lagrangian algorithm is introduced to solve the optimisation problem. Comparing to SUnSAL method and SL0SU method, our experimental results with both simulated and real hyperspectral data sets demonstrate that the RSL0SU method is an effective and accurate spectral unmixing algorithm for hyperspectral remote sensing imagery. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Iterative methods

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Multi-view Metric Learning for Multi-view Video Summarization

Multi-view Metric Learning for Multi-view Video Summarizatio...

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International Conference on Cyberworlds

作者： Linbo Wang Xianyong Fang Yanwen Guo Yanwei Fu MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University China

Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.

关键词： Extraterrestrial measurements Optimization Manifolds Visualization Minimization Eigenvalues and eigenfunctions

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A novel noise reduction method for optical coherence tomography images

A novel noise reduction method for optical coherence tomogra...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiaoming Liu Zhou Yang Jia Wang Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China College of Computer Science and Technology Wuhan University of Science and technology Wuhan China

ISBN: (纸本)9781509037117

In the field of image processing, compared with noisy image, high-quality or clean image usually has a positive impact on the final results, which lead to the emergence of image denoising. Recently, the patch-based methods have attracted considerable interest of researchers, which leverage the self-similarity of the noisy image or information of other relevant images. In this paper, we proposed a novel patch-based method to reduce the noise in optical coherence tomography (OCT) image, which consist of two steps. A combination of internal denoising and external denoising is adopted to select similar patches for each patch in the noisy image. Next, we use low rank technique to denoise the group of the noisy patch and its similar patches. After all patches in a noisy image are denoised, the corresponding clean image is constructed by aggregating all denoised pixels and the first step denoising is accomplished. A new noisy image is constructed and taken as the input of the second step denoising. Experimental results demonstrate that our method achieves better effect and performance, compared with the existing state-of-the-art methods.

关键词： Noise measurement Noise reduction Standards Optical coherence tomography Sparse matrices Databases Image processing

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