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arXiv 2018年

作者： ZHOU, YANG ZHU, ZHEN BAI, XIANG LISCHINSKI, DANI COHEN-OR, DANIEL HUANG, HUI Shenzhen University Huazhong University of Science and Technology Hebrew University of Jerusalem Tel Aviv University College of Computer Science Software Engineering

The real world exhibits an abundance of non-stationary textures. Examples include textures with large scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

arXiv

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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

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Feature Guided Fingerprint Pore Matching 12th

Feature Guided Fingerprint Pore Matching

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Liu, Feng Zhao, Yuanhao Shen, Linlin College of Computer Science and Software Engineering Shenzhen University ShenzhenGuangdong China

ISBN: (纸本)9783319699226

The huge number of sweat pores in fingerprint images results in low efficiency of direct pore (DP) matching methods. To overcome this drawback, this paper proposes a feature guided fingerprint pore matching method. It selects "distinctive" pores around the minutiae and singular points from fingerprint images which extremely reduced the number of pore features for matching. And then, the selected "distinctive" pores are matched using the-state-of-the-art DP matching methods. We also consider to take the select "distinctive" pores together with the extracted minutiae and singular points as a whole feature set for matching. The experimental results have shown that the matching time of the proposed method can be reduced to a quarter of the original time when the recognition accuracy is kept at the same level. Both of the matching time and recognition accuracy are improved when multi-features are taken as a whole set for matching. © 2017, Springer International Publishing AG.

关键词： Biometrics

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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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A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Yue Ma Chenghao Wei Hao Long Key Laboratory of Impression Evidence Examination and Identification Technology (Criminal Investigation Police University of China) Ministry of Public Security China Big Data Institute College of Computer Science and Software Engineering Shenzhen University China

Aiming at the issues that affect the gait recognition, such as outfit changes and carry-on-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy.

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SG-FCN: A motion and memory-based deep learning model for video saliency detection

arXiv

引用

arXiv 2018年

作者： Sun, Meijun Zhou, Ziqi Hu, Qinghua Wang, Zheng Jiang, Jianmin Computer Science Tianjin University Tianjin300350 China School of Software Engineering Tianjin University Tianjin300350 China Research Institute for Future Media Computing College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Improved Collaborative Filtering Algorithm Based on Multifactor Fusion and User Features Clustering

Improved Collaborative Filtering Algorithm Based on Multifac...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Luyan Ni Jiulei Jiang Xiaofeng Wang College of Computer Science and Engineering North Minzu University Yinchuan China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9781538675199

Nowadays, collaborative filtering has become a core technology of personalized recommender systems, but the data sparsity and scalability problems seriously affected the accuracy of similarity computation. To alleviate these problems, a novel effective similarity calculation algorithm combined with user features clustering was proposed in this paper. The newly users similarities calculation model integrated the improved user rating similarity and items category preference similarity. Then, considering the K-means clustering algorithm, we clustered the users based on the user attributes through Euclidean distance so as to the nearest neighbors could be obtained in proper clusters. Experiments on MovieLens dataset are implemented. The experiments show that, compared with other improved algorithms, the method can effectively improve the prediction accuracy and quality of recommendation system.

关键词： Big Data

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Information-centric networking routing challenges and Bio/ACO-inspired solution: A review 9th

Information-centric networking routing challenges and Bio/AC...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhang, Qingyi Wang, Xingwei Huang, Min Lv, Jianhui School of Computer Science and Engineering Northeastern University Shenyang China College of Software Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries School of Information Science and Engineering Northeastern University Shenyang China Central Research Institute Network Technology Laboratory Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9783319938141

Information-Centric Networking (ICN) aims to distribute and retrieve the content by name. In this paper, we review and approve the feasible Ant Colony Optimization (ACO)-inspired ICN routing solutions, i.e., applying ACO to solve ICN routing problem. At first, some significant challenges with respect to ICN routing are analyzed, such as explosive increasing of Forwarding Information Base (FIB), retrieval of closest content copy, uniform distribution of content and mobility support. Then, the solutions inspired by biology feature and behavior is reviewed. In addition, a general design thought of ACO-inspired solution is presented. Finally, the feasibility of ACO-inspired ICN routing solution is evaluated. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Ant colony optimization

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Corrigendum to “Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques” [Renew Sustain Energy Rev 144 (2021) 11096]

引用

Renewable and Sustainable Energy Reviews 2021年 145卷

作者： Ghezlane Halhoul Merabet Mohamed Essaaidi Mohamed Ben Haddou Basheer Qolomany Junaid Qadir Muhammad Anan Ala Al-Fuqaha Mohamed Riduan Abid Driss Benhaddou Smart Systems Laboratory (SSL) ENSIAS Mohammed V University in Rabat 713 Morocco Department of Engineering Technology University of Houston TX 77204 USA MENTIS Consulting SA 13 rue de Congrès 1000 Brussels Belgium Department of Cyber Systems College of Business and Technology University of Nebraska at Kearney (UNK) Kearney NE 68849 USA Information Technology University Lahore 54000 Pakistan Software Engineering Department Alfaisal University-Riyadh Saudi Arabia Information and Computing Technology (ICT) Division College of Science and Engineering (CSE) Hamad Bin Khalifa University Doha Qatar Department of Computer Science Western Michigan University Kalamazoo MI 49008 USA School of Science and Engineering Alakhawayn University in Ifrane 1005 Morocco

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Relaxing of flexible RDF queries: A relative proximity relation-based approach

Relaxing of flexible RDF queries: A relative proximity relat...

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作者： Tong, Qiang Cheng, Jingwei Zhang, Fu College of Software Northeastern University China College of Computer Science and Engineering Northeastern University China

In recent years there have been a great deal of interests in effectively extracting information from RDF repositories. However, users often experience empty answer problems (EAP) when they are not familiar with the structure or content of a RDF dataset and issue a restrictive query to the system. A solution is to allow users to incorporate fuzzy conditions into the query to make it more flexible. Although a fuzzy query is more or less satisfactory, there are still occasions when no or too few entities in a RDF dataset satisfy the query criteria with a degree more than zero, which is called a fuzzy-EAP problem. In this paper, we propose a query relaxation approach based on relative proximity relations to tackle this problem. We present a framework of fuzzy query relaxation over RDF datasets, and present query relaxation algorithms for atomic f-SPARQL queries and complex f-SPARQL queries. Moreover, this paper reports on a prototype implementation on an extension of LUBM dataset to verify the feasibility and effectiveness of the proposed method. The experimental results show a relatively high recall as more relevant results are returned after relaxation. © 2017-IOS Press and the authors. All rights reserved.

关键词： Resource Description Framework (RDF)

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