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IEEE International Conference on Image processing

作者： Jianchao Li Dan Wang Canxiang Yan Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS University of Chinese Academy of Sciences Institute of Deep Learning Baidu Inc.

ISBN: (纸本)9781479983407

Object segmentation has constantly received much attention due to its fundamental role in scene understanding. Traditional methods formulate it as a structured prediction problem, represented by graphical models (GMs). However, most GMs have difficulties in balancing the effectiveness of context modeling and efficiency of model inference. In this paper, we model the contexts implicitly using the deep convolutional neural network (DCNN). Specifically, we reformulate object segmentation as a regression problem and train a deep network end-to-end to learn the nonlinear mapping from the image to the object mask. The large receptive field of the network incorporates wide contexts to update the network parameters, giving an implicit context model. Moreover, the deep architecture is favorable for modeling nonlinearity. The inference of our method is quite efficient, involving only a simple feed-forward pass. Extensive experiments on public datasets demonstrate the advantages of our method.

关键词： Object segmentation Context modeling Deep convolutional neural networks Regression Object segmentation Context modeling Regression (Psychology) network parameters Regression Regression

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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An Improved Precoding of Approximative Matrix Inverse Computations Based on Norm Minimization Algorithm in Massive MIMO System

An Improved Precoding of Approximative Matrix Inverse Comput...

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2015 IEEE International Conference on Progress in Informatics and computing(PIC 2015)

作者： Hongliu Tu Yanjun Hu Yaohua Xu Fengrong Li Key laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences

In massive multiple-input multiple-output（MIMO） system, scaling up the antennas of base station（BS） has a clear benefit on sum rate and energy efficiency, but the signal processing complexity can be very high and many algorithms cannot be implemented in practice for high hardware cost. Approximati ve Matrix Inverse Computations（AMIC） algorithm is a kind of lowcomplexity precoding for large multiuser MIMO systems, but the Bite Error Rate（BER） performance is shown to be not better than the classical MMSE precoding. To improve the BER performance of AMIC algorithm, in this paper, we use norm minimization algorithm to change the coefficient of the precoding matrix to improve the BER performance of AMIC algorithm. It can verify that the proposed algorithm can achieve better BER performance than the AMIC algorithm by using only a limited number of Neumann series iterations, and keep lower complexity. The proposed scheme is a compromise solution between complexity and BER performance.

关键词： Massive MIMO linear precoding algorithm approximative matrix inverse norm minimization

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Fusing PLSA model and Markov random fields for automatic image annotation

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High technology Letters 2014年第4期20卷 409-414页

作者：田东平 Zhao Xiaofei Shi Zhongzhi Institute of Computer Software Baoji University of Arts and Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the *** novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image *** demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

关键词： automatic image annotation probabilistic latent semantic analysis （PLSA） expectation maximization Markov random fields （MRF） image retrieval

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A sound and complete R-calculi with respect to contraction and minimal change

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Frontiers of Computer Science 2014年第2期8卷 184-191页

作者： Wei LI Yuefei SUI State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

AGM postulates are for belief revision （revision by a single belief）, and DP postulates are for iterated revision （revision by a finite sequence of beliefs）. R-calculus is given for R-configurations △｜Г, where △ is a set of atomic formulas or the negations of atomic formulas, and Г is a finite set of formulas. We shall give two R-calculi C and M （sets of de- duction rules） such that for any finite consistent sets Г, △of formulas in the propositional logic, there is a consistent set ⊙ Г C of formulas such that △IГ → △, ⊙ is provable and⊙ is a contraction of F by A or a minimal change of F by A; and prove that C and M are sound and complete with respect to the contraction and the minimal change, respectively.

关键词： belief revision R-calculus minimal change,contraction

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A saliency detection method based on global contrast

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, Image processing and Pattern Recognition 2015年第7期8卷 111-122页

作者： Yu, Chun-Yan Zhang, Wei-Shi Wang, Chun-Li Institute of Information and Science Technology Dalian Maritime University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei China

To highlight the saliency object clearly from the foreground, we propose a saliency detection method based on global contrast with cluster. Due to the fact that background pixels usually have similar patches, we use cluster analysis to merge the background regions. By using mean shift filter, the background pixels with similar color level are clustered and the saliency calculation can be decreased a lot. In the method, we use the contrast of color feature with all the other pixels to compute the saliency map. A weight coefficient is utilized to improve the detection accuracy in global contrast differences evaluation. The results of extensive experiments on public dataset show that our method perform well and can highlight the salient object clearly against the other five state-of-the-art methods. Besides, we demonstrate that the applications in image segmentation and fusion with our saliency map can get satisfactory results. © 2015 SERSC.

关键词： Image segmentation

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An algorithm for reading activity recognition based on electrooculogram

An algorithm for reading activity recognition based on elect...

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International Conference on information, Communications and Signal processing

作者： Rui OuYang Zhao Lv XiaoPei Wu Anhui University Hefei Anhui CN Ministry of Education Key Lab of Intelligent Computing & Signal Processing Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei China

ISBN: (纸本)9781467372190

The research of the relationship between eye-movement and human activity has become a hot spot in the field of the human-computer interaction. In order to recognize the eye-movement patterns related to reading tasks, an algorithm based on electrooculogram(EOG) has been proposed in this paper. In the proposed approach, wavelet packet decomposition was firstly used to smooth EOG signals during reading tasks, and then, according to the predefined coding strategy, the smoothed EOG waveform is translated into a coding string to represent current reading state. Finally, Levenshtein distance is employed to measure the similarity of the template string and the output string. The experiments under the experimental paradigms of autonomy-reading and stimulus-reading and reading with rest state have been carried out. The mean accurate has reached 90%. The experimental results indicate that the proposed algorithm can effectively recognize human reading activity state.

关键词： Electrooculography Wavelet packets Encoding Algorithm design and analysis Electrodes

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MR-COF:A Genetic MapReduce Configuration Optimization Framework

MR-COF:A Genetic MapReduce Configuration Optimization Framew...

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The 7th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP2015(2015年第七届国际并行体系结构,算法和编程研讨会)

作者： Chao Liu Deze Zeng Hong Yao Chengyu Hu Xuesong Yan Yuanyuan Fan China Services Computing Technology and System Lab and Cluster and GridComputing Lab Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China

Hadoop/MapReduce has emerged as a de facto programming framework to explore cloud-computing *** has many configuration parameters,some of which are crucial to the performance of MapReduce *** practice,these parameters... 详细信息

ISBN: (纸本)9783319271392

关键词： Mapreduce Massive data processing Parameter configuration Performance optimization Search algorithm

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Communication Tool for the Hard of Hearings: A Large Vocabulary Sign Language Recognition System

Communication Tool for the Hard of Hearings: A Large Vocabul...

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International Conference on Affective computing and intelligent Interaction

作者： Xiujuan Chai Hanjie Wang Fang Yin Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781479999545

Deaf person has a large social community around the world. The smooth communication is very difficult for these hard of hearings. Automatic Sign Language Recognition (SLR) can build the bridge between the deaf and the hearings and turn the seamless interaction into reality. This paper presents a visualized communication tool for the hard of hearings, i.e. a large vocabulary sign language recognition system based on the RGB-D data input. A novel Grassmann Covariance Matrix (GCM) representation is used to encode a long-term dynamics of a sign sequence and the discriminative kernel SVM is adopted for the sign classification. For continuous sign language recognition, a probability inference method is used to determine the spotting from the lab.ls of sequential frames. Some basic evaluation and comparison of our recognition algorithms are conducted in our collected datasets. This demo will show the recognition of both isolated sign words and the continuous sign language sentences.

关键词： Sign language recognition Sentence spotting Grassmann covariance matrix Kernel SVM RGB-D data

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