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2018 International Conference on Security, pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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An optical image quality evaluation method based on evidence theory

An optical image quality evaluation method based on evidence...

引用

IEEE Chinese Guidance, Navigation and control Conference (CGNCC)

作者： WEN JU JIAOLONG LIU MIAOMIAO ZHANG HONGQUAN YUN Pattern Recognition on National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China Guidance Navigation and Control on National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing China

ISBN: (数字)9781538611715

ISBN: (纸本)9781538611722

Because the image will be interfered by some factors in the process of ingathering, transmission and storage, resulting in poor image quality, the final image quality will not meet its requirement resulting. In this paper, a good method of quality evaluation based on evidence theory is proposed for speedily filtering high-quality image from optical images. Through the analysis and selection of image quality evaluation, the image quality characteristic matrix is fuzzy amalgamated. This method can improve the accuracy of image quality evaluation. The validity of the algorithm is also verified through some experimental tests.

关键词： Image quality Manganese Image edge detection Indexes Aerospace control Electronic mail Standards

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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Sparse Kernel Regression with Coefficient-based `q−regularization

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Journal of Machine Learning Research 2019年 20卷

作者： Shi, Lei Huang, Xiaolin Feng, Yunlong Suykens, Johan A.K. Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai China Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai China Department of Mathematics and Statistics State University of New York at Albany New York United States Department of Electrical Engineering ESAT-STADIUS KU Leuven Kasteelpark Arenberg 10 LeuvenB-3001 Belgium

In this paper, we consider the `q−regularized kernel regression with 0 q−penalty term over a linear span of features generated by a kernel function. We study the asymptotic behavior of the algorithm under the framework of learning theory. The contribution of this paper is two-fold. First, we derive a tight bound on the `2−empirical covering numbers of the related function space involved in the error analysis. Based on this result, we obtain the convergence rates for the `1−regularized kernel regression which is the best so far. Second, for the case 0 ©c 2019 Lei Shi, Xiaolin Huang, Yunlong Feng, and Johan A.K. Suykens.

关键词： Regression analysis

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Forecast of Traffic Vehicle Demand Based on AHP Decision Model

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Journal of Physics: Conference Series 2020年第1期1670卷

作者： Jie Xia Jintao Tan Li Li Musong Gu College of Information Science and Technology Chengdu University Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu China

The development of high technology has driven the rapid growth of civil aviation passenger demand. However, most current studies ignore the impact of increased passenger traffic on ground vehicle demand. This paper studies the demand forecast of transportation vehicles in a specific environment. Taxis are under huge service pressure when faced with the characteristics of irregular time distribution and high dynamics. After the taxi driver escorts the passenger to the airport, it is a big problem whether to leave without a load or go to the car pool. This paper uses Analytic Hierarchy Process (AHP) to construct a judgment matrix, establishes an airport taxi demand forecast model based on the best interests. Taking the status of taxi operation in Kunming Changshui International Airport as an example, the results show that the model can predict taxi demand more accurately and has practical guidance significance. Compared with traditional schemes, it is more conducive to improving driver's income.

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Optimized hidden markov model based on constrained particle swarm optimization

arXiv

引用

arXiv 2018年

作者： Chang, Liu Ouzrout, Yacine Nongaillard, Antoine Bouras, Abdelaziz Jiliu, Zhou DISP Laboratory University Lumiere of Lyon 2 France Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan China College of Information Science and Technology Chengdu University Chengdu China

As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to overcome the statistical constraints in HMM, the paper develops re-normalization and re-mapping mechanisms to ensure the constraints in HMM. The experiments have shown that PSOHMM can search better solution than BWHMM, and has faster convergence speed. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

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Face recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

引用

2nd International Conference on Image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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3D Attention Network(3DAN)to Capture Candidate Biomarkers for Alzheimer's Disease

3D Attention Network(3DAN)to Capture Candidate Biomarkers fo...

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2019中国阿尔茨海默病论坛(中国脑病大会CCBD2019)

作者： Dan Jin Bo Zhou Ying Han Jiaji Ren Tong Han Bing Liu Jie Lu Chengyuan Song Pan Wang Dawei Wang Jian Xu Zhengyi Yang Hongxiang Yao Chunshui Yu Kun Zhao Xinqing Zhang Yuying Zhou Xi Zhang Tianzi Jiang Qing Wang Yong Liu Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Neurology the Second Medical CentreNational Clinical Research Centre for Geriatric Di Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijingChina Center of Alzhei Department of Radiology Tianjin Huanhu HospitalTianjinChina Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Radiology Xuanwu Hospital of Capital Medical UniversityBeijingChina Department of Neurology Qilu Hospital of Shandong UniversityJi'nanChina Department of Neurology Tianjin Huanhu HospitalTianjinChina Department of Radiology Qilu Hospital of Shandong UniversityJi'nanChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese A Department of Radiology Chinese PLA General HospitalBeijingChina Department of Radiology Tianjin Medical University General HospitalTianjinChina Brainnetome Center&National Laboratory of Pattern Recognition Institute of AutomationChinese Academ Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijingChina Alzheimer's Disease Neuroimaging Initiative Multi-Centre Alzheimer Disease Neuroimaging Working Group

Brain structural alterations are promising biomarkers for tracking disease progression and diagnosing Alzheimer's disease(AD).Deep learning methods have been increasingly used for computer-aided diagnosis of AD due to its learning ability to optimize feature representation and ***,a major limitation of the existing methods is the lack of availability of valuable spatial information which is isolated from subsequent feature optimization and classification processes.

关键词： Attention mechanism Alzheimer's disease Computer-aided diagnosis Structural magnetic resonance imaging Neurobiological basis

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J-Measure Based Pruning for Advancing Classification Performance of Information Entropy Based Rule Generation

J-Measure Based Pruning for Advancing Classification Perform...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Han Liu Mihaela Cocea Weili Ding School of Computer Science and Informatics Cardiff University Queen’s Buildings 5 The Parade Cardiff United Kingdom School of computing University of Portsmouth Buckingham Building Lion Terrace Portsmouth United Kingdom Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Heibei Provience Yanshan University Qinghuangdao China

ISBN: (纸本)9781538652152

Learning of classification rules is a popular approach of machine learning, which can be achieved through two strategies, namely divide-and-conquer and separate-and-conquer. The former is aimed at generating rules in the form of a decision tree, whereas the latter generates if-then rules directly from training data. From this point of view, the above two strategies are referred to as decision tree learning and rule learning, respectively. Both learning strategies can lead to production of complex rule based classifiers that overfit training data, which has motivated researchers to develop pruning algorithms towards reduction of overfitting. In this paper, we propose a J-measure based pruning algorithm, which is referred to as Jmean-pruning. The proposed pruning algorithm is used to advance the performance of the information entropy based rule generation method that follows the separate and conquer strategy. An experimental study is reported to show how Jmean-pruning can effectively help the above rule learning method avoid overfitting. The results show that the use of Jmean-pruning achieves to advance the performance of the rule learning method and the improved performance is very comparable or even considerably better than the one of C4.5.

关键词： Decision trees Learning systems Training data Entropy Information entropy Machine learning Machine learning algorithms

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