检索结果-内蒙古大学图书馆

Image Enhancement in Varying Light Conditions Based on Wavelet Transform

学校读者我要写书评

暂无评论

Image Enhancement in Varying Light Conditions Based on Wavel...

International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： MUHAMMAD AAMIR ZIAUR REHMAN YI-FEI PU AWAIS AHMED WAHEED AHMED ABRO Tiansi Intelligent Systems Research Institute College of Computer Science Sichuan University Chengdu China Information and Software Engineering University of Electronic Science and Technology Chengdu China School of Computer Science and Engineering Southeast University Nanjing China

The following topics are dealt with: learning (artificial intelligence); neural nets; pattern classification; feature extraction; optimisation; computer vision; convolutional neural nets; health care; gradient methods... 详细信息

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

关键词： Image color analysis Image enhancement Wavelet transforms Image edge detection Colored noise Lighting

Align deep features for oriented object detection

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Han, Jiaming Ding, Jian Li, Jie Xia, Gui-Song State Key Lab. LIESMARS and the Institute of Artificial Intelligence Wuhan University Wuhan430079 China Shanghai Aerospace Electronic Technology Institute Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence State Key Lab. LIESMARS Wuhan University Wuhan430072 China

—The past decade has witnessed significant progress on detecting objects in aerial images that are often distributed with large scale variations and arbitrary orientations. However most of existing methods rely on heuristically defined anchors with different scales, angles and aspect ratios and usually suffer from severe misalignment between anchor boxes and axis-aligned convolutional features, which leads to the common inconsistency between the classification score and localization accuracy. To address this issue, we propose a Single-shot Alignment Network (S2A-Net) consisting of two modules: a Feature Alignment Module (FAM) and an Oriented Detection Module (ODM). The FAM can generate high-quality anchors with an Anchor Refinement Network and adaptively align the convolutional features according to the anchor boxes with a novel Alignment Convolution. The ODM first adopts active rotating filters to encode the orientation information and then produces orientation-sensitive and orientation-invariant features to alleviate the inconsistency between classification score and localization accuracy. Besides, we further explore the approach to detect objects in large-size images, which leads to a better trade-off between speed and accuracy. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on two commonly used aerial objects datasets (i.e., DOTA and HRSC2016) while keeping high efficiency1 Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

Multiphase Focal Liver Lesions Classification with Combined N-gram and BoVW

学校读者我要写书评

暂无评论

Multiphase Focal Liver Lesions Classification with Combined ...

KES International conferences on Innovation in Medicine and Healthcare, KES-InMed 2019 and Intelligent Interactive Multimedia Systems and Services, KES-IIMSS 2019

作者： Huang, Huimin Ji, Zhiyuan Lin, Lanfen Liao, Ziliang Chen, Qingqing Hu, Hongjie Zhang, Qiaowei Iwamoto, Yutaro Chen, Yen-Wei Tong, Ruofeng Wu, Jian Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China School of Medicine Zhejiang University Hangzhou China Department of Radiology Sir Run Run Shaw Hospital Hangzhou China College of Information Science and Engineering Ritsumeikan University Kyoto Japan

ISBN: (纸本)9789811385650

The bag-of-visual-words (BoVW) model has emerged as an effective approach to represent features for focal liver lesions (FLLs). However, most of the previous methods have the limitation of insufficient consideration of the spatiotemporal co-occurrence information, which provokes the low descriptive power of classic visual words. In contrast to previous work, we propose a novel model for multiphase medical image feature generation named the Bi-gram bag-of-spatiotemporal words (Bi-gram BoSTW) to capture the temporal information, as well as, the spatial co-occurrence relationship of the lesion. First, temporal co-occurrence images from multiphase images are constructed. Second, BoVW is employed to extract temporal features from the temporal co-occurrence images and generates the visual words. Finally, we introduce the N-gram schema to add spatial relation to local descriptors. To the best of our knowledge, this is the first work that introduces visual N-grams scheme to contrast-enhanced CT images, which integrates temporal information with spatial co-occurrence relationship and improves the classification performance. The effectiveness of the proposed model is verified on 132 FLLs with confirmed pathology type. The experimental results indicate that (1) the N-gram enriches the semantics and provides more complete representation;(2) the proposed model achieves the best accuracy (83%) with highest training speed (1.5Â min) among several well-known methods based on BoVW model. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

A big data enabled channel model for 5G wireless communication systems

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Huang, Jie Wang, Cheng-Xiang Bai, Lu Sun, Jian Yang, Yang Li, Jie Tirkkonen, Olav Zhou, Ming-Tuo National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Cyber Science and Technology Beihang University Beijing100191 China Shandong Provincial Key Lab of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao266237 China ShanghaiTech University Shanghai201210 China Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai200240 China Aalto University Espoo02150 Finland Shanghai200050 China

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

PHD: A Deep Learning Based Human Detection Framework for Panoramic Videos

学校读者我要写书评

暂无评论

PHD: A Deep Learning Based Human Detection Framework for Pan...

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jinting Tang Zhenhui Chen Yongkai Huo Peichang Zhang National Engineering Laboratory for Big Data System Computing Technology Research Institute for Future Media Computing School of Computer Science and Software Engineering Shenzhen University Shenzhen China The College of Information Engineering Shenzhen University Shenzhen China

Panoramic video has attracted substantial research attention as the coming video format. It is capable of providing 360 degree immersive experience of omnidirectional visual information. State-of-the-art detection networks may fail to detect humans on spherical images, which are normally represented in deformed rectangular shapes. In this paper, we propose a socalled Panoramic Human Detection (PHD) scheme to address the task of human detection in panoramic videos. Moreover, the PHD method is designed to detect humans by extracting multiple overlapping sub-images from each integral spherical image, where three-dimensional rotation of spherical images is employed to ensure consistency of sub-images. Two detection box filters are designed for removing redundant boxes. Our PHD method is capable of accomplishing the task of human detection in various panoramic video types. Experiments prove that our PHD method outperforms the baseline by 35% and 48.6% in terms of precision and recall, respectively.

关键词： Videos Task analysis Feature extraction Matrices Visualization Strain Transmission line matrix methods

Shape robust text detection with progressive scale expansion network

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wang, Wenhai Xie, Enze Li, Xiang Hou, Wenbo Lu, Tong Yu, Gang Shao, Shuai National Key Lab for Novel Software Technology Nanjing University Department of Comuter Science and Technology Tongji University School of Computer and Engineering Nanjing University of Science and Technology Momenta Technology Inc PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical detection

An Approach to Source Code Plagiarism Detection Based on Abstract Implementation Structure Diagram 2

学校读者我要写书评

暂无评论

An Approach to Source Code Plagiarism Detection Based on Abs...

2nd International Conference on Electronic information technology and computer engineering, EITCE 2018

作者： Guo, Shuang Liu, Jianbin School of Computer Science Beijing Information Science and Technology University Beijing100101 China Software Engineering Research Center Beijing Information Science and Technology University Beijing100101 China

Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Based on the analysis of the characteristics and defects of the existing program code similarity detection system, a method of source code similarity detection based on Abstract Implementation Structure Diagram (AISD) is proposed. The source code modelling and format into an abstract implementation structure diagram, and forming structural feature strings and variable reference relationship sequences by extracting structural features and variable position features. We calculate the overall similarity by calculating structural similarity and variable similarity. The results demonstrate that the performance of the proposed AISD-based approach overcomes other approaches on the same source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection. © The Authors, published by EDP Sciences, 2018.

关键词： Intellectual property

Assessing Mental Stress Based on Smartphone Sensing Data: An Empirical Study

学校读者我要写书评

暂无评论

Assessing Mental Stress Based on Smartphone Sensing Data: An...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets Data models Biomedical monitoring