检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Zhou, Yanning Onder, Omer Fahri Dou, Qi Tsougenis, Efstratios Chen, Hao Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Imsight Medical Technology Co. Ltd. Hong Kong Hong Kong Department of Computing Imperial College London London United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multilevel information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant margin. Copyright © 2019, The Authors. All rights reserved.

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An active learning approach for reducing annotation cost in skin lesion analysis

arXiv

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arXiv 2019年

作者： Shi, Xueying Dou, Qi Xue, Cheng Qin, Jing Chen, Hao Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Imsight Medical Technology Co. Ltd. Shenzhen China Centre for Smart Health School of Nursing Hong Kong Polytechnic University Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a novel active learning framework for cost-effective skin lesion analysis. The goal is to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance. Our sample selection criteria complementarily consider both informativeness and representativeness, derived from decoupled aspects of measuring model certainty and covering sample diversity. To make wise use of the selected samples, we further design a simple yet effective strategy to aggregate intra-class images in pixel space, as a new form of data augmentation. We validate our proposed method on data of ISIC 2017 Skin Lesion Classification Challenge for two tasks. Using only up to 50% of samples, our approach can achieve state-of-the-art performances on both tasks, which are comparable or exceeding the accuracies with full-data training, and outperform other well-known active learning methods by a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost effectiveness

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A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

A real-time unsupervised background extraction-based target ...

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作者： Li, Cong Gao, Lianru Wu, Yuanfeng Zhang, Bing Plaza, Javier Plaza, Antonio Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China The College of Computer Science and Software Engineering Computer Vision Research Institute Shenzhen University Shenzhen518060 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica de Cáceres University of Extremadura Cáceres Spain

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-ti

关键词： Field programmable gate arrays (FPGA)

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Variable population memetic search: A case study on the critical node problem

arXiv

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arXiv 2019年

作者： Zhou, Yangming Hao, Jin-Kao Fu, Zhang-Hua Wang, Zhe Lai, Xiangjing School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Department of Computer Science LERIA Université d'Angers 2 Boulevard Lavoisier Angers49045 France Institut Universitaire de France 1 rue Descartes Paris75231 France Robotics Laboratory for Logistics Service Institute of Robotics and Intelligent Manufacturing Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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Author Correction: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

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NPJ digital medicine 2022年第1期5卷 38页

作者： Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S C Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M Zangwill Alejandro F Frangi Tin Aung Ching-Yu Cheng Yu Qiao Xiulan Zhang Daniel S W Ting State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangzhou People's Republic of China. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sciences Shenzhen People's Republic of China. University of Chinese Academy of Sciences Beijing People's Republic of China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. gxtykyy@***. Handan City Eye Hospital Handan People's Republic of China. C-MER (Shenzhen) Dennis Lam Eye Hospital International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen) Shenzhen People's Republic of China. The Eye Hospital WMU at Hangzhou Hangzhou People's Republic of China. Department of Ophthalmology The Second Hospital of Jilin University Changchun People's Republic of China. SenseTime Group Limited Hong Kong People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Guizhou Medical University Kaili People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an People's Republic of China. Department of Ophthalmology The Third Affiliated Hospital of Nanchang University Nanchang People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology UC San Diego La Jolla CA United States. CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds UK. Singapore Eye Research Institute and Singapore National Eye Centre Singapore Singapore. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sci

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SoccerNet 2022 Challenges Results

arXiv

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arXiv 2022年

作者： Giancola, Silvio Cioppa, Anthony Deliège, Adrien Magera, Floriane Somers, Vladimir Kang, Le Zhou, Xin Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Darwish, Abdulrahman Maglo, Adrien Clapés, Albert Luyts, Andreas Boiarov, Andrei Xarles, Artur Orcesi, Astrid Shah, Avijit Fan, Baoyu Comandur, Bharath Chen, Chen Zhang, Chen Zhao, Chen Lin, Chengzhi Chan, Cheuk-Yiu Hui, Chun-Chuen Li, Dengjie Yang, Fan Liang, Fan Da, Fang Yan, Feng Yu, Fufu Wang, Guanshuo Chan, H. Anthony Zhu, He Kan, Hongwei Chu, Jiaming Hu, Jianming Gu, Jianyang Chen, Jin Soares, João V.B. Theiner, Jonas De Corte, Jorge Brito, José Henrique Zhang, Jun Li, Junjie Liang, Junwei Shen, Leqi Ma, Lin Chen, Lingchi Marques, Miguel Santos Azatov, Mike Kasatkin, Nikita Wang, Ning Jia, Qiong Pham, Quoc-Cuong Ewerth, Ralph Song, Ran Li, Rengang Gade, Rikke Debien, Ruben Zhang, Runze Lee, Sangrok Escalera, Sergio Jiang, Shan Odashima, Shigeyuki Chen, Shimin Masui, Shoichi Ding, Shouhong Chan, Sin-Wai Chen, Siyu El-Shabrawy, Tallal He, Tao Moeslund, Thomas B. Siu, Wan-Chi Zhang, Wei Li, Wei Wang, Xiangwei Tan, Xiao Li, Xiaochuan Wei, Xiaolin Ye, Xiaoqing Liu, Xing Wang, Xinying Guo, Yandong Zhao, Yaqian Yu, Yi Li, Yingying He, Yue Zhong, Yujie Guo, Zhenhua Li, Zhiheng KAUST Thuwal Saudi Arabia University of Liège Liège Belgium EVS Broadcast Equipment Liège Belgium Sportradar London United Kingdom UCLouvain London United Kingdom EPFL London United Kingdom Baidu Research Sunnyvale United States UCLouvain Louvain-la-Neuve Belgium EPFL Lausanne Switzerland German University in Cairo New Cairo City Egypt Université Paris-Saclay CEA List Paris France Aalborg University Aalborg Denmark ReBatch Kontich Belgium Schaffhausen Institute of Technology Schaffhausen Switzerland Universitat de Barcelona Barcelona Spain Yahoo Research Sunnyvale United States Inspur Electronic Information Industry Co. Ltd. State Key Laboratory of High-end Server Storage Technology Jinan China Purdue University West Lafayette United States OPPO Research Institute Shenzhen China Baidu Inc. Beijing China Sun Yat-sen University Guangzhou China Caritas Institute of Higher Education Tseung Kwan O Hong Kong Meituan Inc. Beijing China Fujitsu Research Kawasaki Japan QCraft Inc. Beijing China Tencent Youtu Lab Shanghai China Tsinghua University Beijing China Beijing University of Posts and Telecommunications Shenzhen China Zhejiang University Shenzhen China MGTV Changsha China L3S Research Center Leibniz University Hannover Hannover Germany 2Ai - School of Technology IPCA São Martinho Portugal Shanghai Jiao Tong University Shanghai China Arsenal FC London United Kingdom East China University of Science and Technology Shenzhen China L3S Research Center Leibniz University Hannover Hannover Germany TIB - Leibniz Information Center for Science and Technology Hannover Germany School of Control Science and Engineering Shandong University Jinan China Graduate School of Information Yonsei University Seoul Korea Republic of MODULABS Seoul Korea Republic of Computer Vision Center Barcelona Spain Aalborg University Barcelona Spain Hong Kong Polytechnic University Tseung Kwan O Hong Kong Baidu Inc. Beijing China

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://***. Baselines and development kits are available on https://***/SoccerNet. © 2022, CC BY.

关键词： computer vision

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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results 16th

AIM 2020 Challenge on Efficient Super-Resolution: Methods an...

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Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sung-Ho Kim, Yongwoo Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Zürich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO Nanjing China University of Electronic Science and Technology of China Nanjing China McMaster University Hamilton Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China Xiamen University Xiamen China Indian Institute of Technology Madras Chennai India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University Xi’an China Kyung Hee University Seoul Korea Republic of Sang Myung University Seoul Korea Republic of Yonsei University Seoul Korea Republic of ETH Zurich Zürich Switzerland Zhejiang University Hangzhou China Shandong China Xidian University Xi’an China Samsung AI Centre Toronto Canada Wuhan China Dalian Maritime University Dalian China China Everbright Bank Co. Ltd. Beijing China College of Engineering Trivandrum India Jinan China InnoPeak Technology Inc. Palo Alto United States SenseTime Research Hong Kong China Sun Yat-sen University Guangzhou China City University of Hong Kong Kowloon Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Udine Italy

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. © 2020, Springer Nature Switzerland AG.

关键词： Optical resolving power

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Millimeter-wave radar image analysis for the traffic sensing

引用

Journal of Physics: Conference Series 2020年第5期1507卷

作者： H M Wu F Qi J K Wang Y Wang School of communication science and engineering Northeastern University Liaoning Province Shenyang 110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength.

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Fingerprint Segmentation via Convolutional Neural Networks 12th

Fingerprint Segmentation via Convolutional Neural Networks

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12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Dai, Xiaowei Liang, Jie Zhao, Qijun Liu, Feng National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China School of Computer Science and Software Engineering Shenzhen University ShenzhenGuangdong China

ISBN: (纸本)9783319699226

In automatic fingerprint identification systems, it is crucial to segment the fingerprint images. Inspired by the superiority of convolutional neural networks for various classification and regression tasks, we approach fingerprint segmentation as a binary classification problem and propose a convolutional neural network based method for fingerprint segmentation. Given a fingerprint image, we first apply the total variation model to decompose it into cartoon and texture components. Then, the obtained texture component image is divided into overlapping patches, which are classified by the trained convolutional neural network as either foreground or background. Based on the classification results and by applying morphology-based post-processing, we get the final segmentation result for the whole fingerprint image. In the experiments, we investigate the effect of different patch sizes on the segmentation performance, and compare the proposed method with state-of-the-art algorithms on FVC2000, FVC2002 and FVC2004. Experimental results demonstrate that the proposed method outperforms existing algorithms. © 2017, Springer International Publishing AG.

关键词： Convolution

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