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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9712 LNCS卷 645-652页

作者： Zheng, Xiaohui Ning, Yi Chen, Xianjun Zhan, Yongsong Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9783319409993

Surveillance video is characterized by large amount of data and redundancy, which makes the suspicious face detection to be a problem. To solve the problem above we proposed suspicious face detection based on key frame. Surveillance video has the type of fixed background, so this paper used the frame difference method with low computational complexity and small computation to extract the key frame. We proposed a new method combined DPM with skin color detection to detect the suspicious face in the key frames. To solve the speed bottleneck of the traditional DPM, we proposed to use the fast HOG LUT feature extraction and the near optimal cost sensitive decision making improving the traditional method. Meanwhile we used YCrCb + otsu skin color segmentation. Since the otsu is easily affected by the illumination, we proposed an improved skin color detection. Experiment results show that the proposed algorithms are robust and accurate for real-time. © Springer International Publishing Switzerland 2016.

关键词： Face recognition

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Imaging inverse problem using sparse representation with adaptive dictionary learning

Imaging inverse problem using sparse representation with ada...

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IEEE International Advance Computing Conference, IACC

作者： Mittu George P Vivek M Joonki Paik The Oxford College of Engineering Bangalore Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul South Korea

ISBN: (纸本)9781479980482

Sparse representation is one of the powerful emerging statistical techniques for modeling images. This representation approximates the image as a combination of the codewords within a dictionary which is over complete. Recent years have seen a tremendous growth in the field of sparse representation. Here a method is proposed for image inversing by using iterative deblurring based on sparse representation of an image which is uniformly blurred. The key idea behind this methodology is the sparseness of natural images in some domain. The quality of recovered image majorly depends on the domain or the dictionaries that are used to represent it. In this paper, the K-SVD algorithm is used to train the group of codewords from a set of quality natural image patches. For each of the local patch within the blurred image, the best suited sub-dictionary from the trained dictionary data base is selected. In addition, a smoothness regularization constraint is added that prevents the reblurring of the image edges. For numerical stability the sparsity weight is adaptively computed which also improves the reconstructed image quality. Comparative study on some of the existing restoration algorithm proves the proposed method outperforms them all.

关键词： Dictionaries Adaptation models image reconstruction Minimization image restoration High definition video Inverse problems

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An efficient MRF embedded level set method for image segmentation

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IEEE Transactions on image processing 2015年第1期24卷 9-21页

作者： Yang, Xi Gao, Xinbo Tao, Dacheng Li, Xuelong Li, Jie State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia State Key Laboratory of Transient Optics and Photonics Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an710119 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an710071 China

This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 x 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory. © 2014 IEEE.

关键词： image segmentation

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Adaptive regularized multichannel Blind deconvolution using alternating minimization

Adaptive regularized multichannel Blind deconvolution using ...

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IEEE International Advance Computing Conference, IACC

作者： Soniya James Vivek Maik Joonki Paik Department of Electronics and Communication The Oxford College of Engineering Bangalore India Image processing and Intelligent Systems Laboratory Chung-ang university Korea

ISBN: (纸本)9781479980482

Multichannel blind deconvolution is an ill posed problem where regularizers plays an important role. Adaptive regularization is used to obtain better quality restored image. This adds penalty weighted term along with image regularizer. The Isotropic TV image regularizers have chosen along with directional priors which preserves the over smoothing of the edges when compare to horizontal and vertical priors, thereby improving the Peak Signal to Noise Ratio(PSNR) of the reconstructed image. The Blind deconvolution problem can be solved using Alternating minimization algorithm as it is best suited for two unknown variables. The formulation of blind deconvolution can be l 1 regularized optimization and geometry can be l 2 . It results in hard optimization problem. The minimization step alternates between two sub optimization problems and this can be solve efficiently using Augmented Lagrangian method(ALM), where each iteration undergoes Bregman variable splitting or iterative method. This converted unconstrained problem in to constrained. This method can be applicable to medical images such as X Ray in order to reduce the amount of rays penetrating in to the body and to observe more details.

关键词： Minimization Deconvolution Optimization image restoration TV image reconstruction Convergence

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A heuristically accelerated multi-agent reinforcement learning algorithm applied to flow line maintenance scheduling

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Journal of Computational Information systems 2015年第6期11卷 2245-2255页

作者： Wang, Xiao Wang, Hongwei Qi, Chao Wang, Xu School of Safety Engineering Shenyang Aerospace University Shenyang China Institute of Systems Engineering/State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

This paper investigates the maintenance scheduling problem in a flow line system consisting of two series machines with a finite buffer in between. The machines deteriorate with age and have multiple deteriorating quality states represented by multiple decreasing yield levels. The maintenance problem of both machines is modeled as semi-Markov decision processes. A heuristically accelerated multi-agent reinforcement learning algorithm with a feature of incorporating heuristic function for action selection is developed to produce a reasonable maintenance scheduling. A comparison with a Ε-greedy search based exploration algorithm, a simulated annealing search based exploration algorithm, a tabu search based exploration algorithm and a neighborhood search based exploration algorithm in reinforcement learning is presented. The results indicate that the proposed heuristically accelerated multi-agent reinforcement learning algorithm is competitive and has a certain advantage for larger problems. Copyright © 2015 Binary Information Press.

关键词： Tabu search

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Motion control and fall detection of intelligent cane robot

Springer Tracts in Advanced Robotics

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Springer Tracts in Advanced Robotics 2015年 106卷 317-337页

作者： Fukuda, Toshio Huang, Jian Di, Pei Sekiyama, Kosuge Key Laboratory of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology No. 1037 Luoyu Road Wuhan Hubei430074 China Department of Micro-Nano Systems Engineering Nagoya University Furo-cho Chikusa-ku Nagoya464-8603 Japan

An intelligent cane robot was designed for aiding the elderly and handicapped people walking. The robot consists of a stick, a group of sensors and an omni-directional basis driven by three Swedish wheels. Multiple sensors were used to recognize the user’s "walking intention", which is quantitatively described by a new concept called intentional direction (ITD). Based on the guidance of filtered ITD, a novel intentionbased admittance motion control (IBAC) scheme was proposed for the cane robot. The experiment results demonstrated that the user feels more natural and comfortable when walking with the assistance of cane robot controlled by the IBAC strategy. To detect the fall of user, a detection method based on Dubois possibility theory was proposed using the combined sensor information from force sensors and a laser ranger finder (LRF) fixed on the cane robot. The human fall model was represented in a two-dimensional space, where the relative position between the Zero- Moment-Point (ZMP) and the center of support triangle was utilized as a significant feature. The effectiveness of proposed fall detection method was also confirmed by experiments. © Springer International Publishing Switzerland 2015.

关键词： Motion control

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Design and performance analysis of a biomedical implantable patch antenna

Design and performance analysis of a biomedical implantable ...

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International Conference on Advances in Electrical Engineering (ICAEE)

作者： Tamim Hossain Reazuddin Mahmud Hajmina Khanom Juhi Mohammad Rakin Imtiaz Rashedul Hoque Image Processing and Intelligent Systems Laboratory Multimedia and Film Chung-Ang University Seoul Korea University of Kiel Germany Christian-Albrechts-University Kiel Germany Future Technology R&D Division Video Technology Lab.

Implantable Medical Devices (IMDs) consist of miniature implantable antenna that is embodied inside and its bidirectional communication is monitored through external monitoring device. This paper presents an implantable miniaturized antenna working in the Medical Implant Communication Service (MICS) band and being carried out in human body phantom models representing implantation inside the arm which helped to design the proposed antenna. Furthermore, different characteristics of the antenna such as S parameter, return loss and directivity were contemplated using FR4 substrate material in the same band.

关键词： Antennas Monitoring Biomedical monitoring Biological system modeling Medical diagnostic imaging Muscles Thermal conductivity

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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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Example-based super-resolution using self-patches and approximated constrained least squares filter

Example-based super-resolution using self-patches and approx...

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作者： Cho, Changhun Jeon, Jaehwan Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Republic of

ISBN: (纸本)9781479957514

This paper presents a novel super-resolution (SR) algorithm using local self-examples. The proposed algorithm consists of three steps: i) generation of the patch dictionary using multiple-step image blurring, ii) search of the optimum patches using the magnitude and orientation of the image gradient, and iii) combination of the restored and original patches for reducing the patch-mismatching error. Example-based SR methods have a common disadvantage of unnaturally reconstructed edges. The proposed method can reconstruct realistic images by searching patches based on the edge strength in dictionary made by multiple-step degradations. Experimental results show that the proposed SR algorithm provides more natural images with less synthetic artifacts than existing methods. The proposed SR method provides significant improvement in both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM). © 2014 IEEE.

关键词： image reconstruction

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