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IEEE International Conference on Consumer Electronics, ICCE 2016

作者： Jeon, Semi Yoon, Inhye Yang, Seung'I Kim, Bongmo Kim, Jisung Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of Video Technology Lab. Future Technology RandD Division SK Telecom Korea Republic of

ISBN: (纸本)9781467383646

Compact, portable digital cameras have been popular to consumers. This paper presents a robust feature detection method using particle keypoints and its application to video stabilization. The proposed video stabilization algorithm consists of three steps: i) generation of a flat region map based on the Gaussian filter, ii) robust feature point detection using particle keypoints, and iii) camera paths estimation for enhancing a shaky video. As a result, the proposed algorithm can estimate optimal homography by redefining important feature points using particle keypoints in the flat region map. The proposed robust feature detection algorithm is suitable for enhancing the quality of video acquired by consumer handheld imaging devices. © 2016 IEEE.

关键词： Stabilization

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

引用

arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Body appreciation around the world: Measurement invariance of the Body Appreciation Scale-2 (BAS-2) across 65 nations, 40 languages, gender identities, and age

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Body image 2023年 46卷 449-466页

作者： Viren Swami Ulrich S Tran Stefan Stieger Toivo Aavik Hamed Abdollahpour Ranjbar Sulaiman Olanrewaju Adebayo Reza Afhami Oli Ahmed Annie Aimé Marwan Akel Hussam Al Halbusi George Alexias Khawla F Ali Nursel Alp-Dal Anas B Alsalhani Sara Álvares-Solas Ana Carolina Soares Amaral Sonny Andrianto Trefor Aspden Marios Argyrides John Jamir Benzon R Aruta Stephen Atkin Olusola Ayandele Migle Baceviciene Radvan Bahbouh Andrea Ballesio David Barron Ashleigh Bellard Sóley Sesselja Bender Kerime Derya Beydağ Gorana Birovljević Marie-Ève Blackburn Teresita Borja-Alvarez Joanna Borowiec Miroslava Bozogáňová Solfrid Bratland-Sanda Matthew H E M Browning Anna Brytek-Matera Marina Burakova Yeliz Çakır-Koçak Pablo Camacho Vittorio Emanuele Camilleri Valentina Cazzato Silvia Cerea Apitchaya Chaiwutikornwanich Trawin Chaleeraktrakoon Tim Chambers Qing-Wei Chen Xin Chen Chin-Lung Chien Phatthanakit Chobthamkit Bovornpot Choompunuch Emilio J Compte Jennifer Corrigan Getrude Cosmas Richard G Cowden Kamila Czepczor-Bernat Marcin Czub Wanderson Roberto da Silva Mahboubeh Dadfar Simon E Dalley Lionel Dany Jesus Alfonso D Datu Pedro Henrique Berbert de Carvalho Gabriel Lins de Holanda Coelho Avila Odia S De Jesus Sonia Harzallah Debbabi Sandesh Dhakal Francesca Di Bernardo Donka D Dimitrova Jacinthe Dion Barnaby Dixson Stacey M Donofrio Marius Drysch Hongfei Du Angel M Dzhambov Claire El-Jor Violeta Enea Mehmet Eskin Farinaz Farbod Lorleen Farrugia Leonie Fian Maryanne L Fisher Michał Folwarczny David A Frederick Matthew Fuller-Tyszkiewicz Adrian Furnham Antonio Alías García Shulamit Geller Marta Ghisi Alireza Ghorbani Maria Angeles Gomez Martinez Sarah Gradidge Sylvie Graf Caterina Grano Gyöngyvér Gyene Souheil Hallit Motasem Hamdan Jonathan E Handelzalts Paul H P Hanel Steven R Hawks Issa Hekmati Mai Helmy Tetiana Hill Farah Hina Geraldine Holenweger Martina Hřebíčková Olasupo Augustine Ijabadeniyi Asma Imam Başak İnce Natalia Irrazabal Rasa Jankauskiene Ding-Yu Jiang Micaela Jiménez-Borja Verónica Jiménez-Borja Evan M Johnson Veljko Jov School of Psychology and Sport Science Anglia Ruskin University Cambridge United Kingdom Centre for Psychological Medicine Perdana University Kuala Lumpur Malaysia. Electronic address: viren.swami@aru.ac.uk. Department of Cognition Emotion and Methods in Psychology Faculty of Psychology University of Vienna Vienna Austria. Department of Psychology and Psychodynamics Karl Landsteiner University of Health Sciences Krems an der Donau Austria. Institute of Psychology University of Tartu Tartu Estonia. Department of Psychology Koç University Istanbul Turkiye. Department of Psychology and Behavioural Studies Ekiti State University Ado-Ekiti Nigeria. Department of Art Studies Tarbiat Modares University Tehran Iran. Department of Psychology University of Chittagong Chattogram Bangladesh. Department of Psychoeducation and Psychology Université du Québec en Outaouais Saint-Jérôme Canada. INSPECT-LB: National Institute of Public Health Clinical Epidemiology and Toxicology Beirut Lebanon School of Pharmacy Lebanese International University Beirut Lebanon. Department of Management Ahmed Bin Mohammad Military College Doha Qatar. Faculty of Psychology Panteion University of Social and Political Sciences Athens Greece. Royal College of Surgeons Ireland-Bahrain Adliya Bahrain. Department of Midwifery Faculty of Health Sciences Munzur University Tunceli Turkiya. Department of Oral Medicine and Diagnostic Sciences Vision College of Dentistry and Nursing Vision Colleges Riyadh Saudi Arabia. Facultad de Ciencias de la Vida Universidad Regional Amazónica Ikiam Muyuna Ecuador. Federal Institute of Education Science and Technology of Southeast Minas Gerais Barbacena Brazil. Department of Psychology Universitas Islam Indonesia Yogyakarta Indonesia. Department of Psychology Aberystwyth University Ceredigion United Kingdom. Department of Psychology Neapolis University Pafos Paphos Cyprus. Department of Psychology De La Salle University Phillippines. Department of Gen

The Body Appreciation Scale-2 (BAS-2) is a widely used measure of a core facet of the positive body image construct. However, extant research concerning measurement invariance of the BAS-2 across a large number of nations remains limited. Here, we utilised the Body image in Nature (BINS) dataset - with data collected between 2020 and 2022 - to assess measurement invariance of the BAS-2 across 65 nations, 40 languages, gender identities, and age groups. Multi-group confirmatory factor analysis indicated that full scalar invariance was upheld across all nations, languages, gender identities, and age groups, suggesting that the unidimensional BAS-2 model has widespread applicability. There were large differences across nations and languages in latent body appreciation, while differences across gender identities and age groups were negligible-to-small. Additionally, greater body appreciation was significantly associated with higher life satisfaction, being single (versus being married or in a committed relationship), and greater rurality (versus urbanicity). Across a subset of nations where nation-level data were available, greater body appreciation was also significantly associated with greater cultural distance from the United States and greater relative income inequality. These findings suggest that the BAS-2 likely captures a near-universal conceptualisation of the body appreciation construct, which should facilitate further cross-cultural research.

关键词： Body appreciation Body appreciation Scale-2 (BAS-2) Cross-cultural Measurement invariance Multi-group confirmatory factor analysis (MG-CFA) Psychometrics Structural analysis

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

引用

arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Electroencephalogram Signal Analysis Based on the Improved k-nearest Neighbor Network

Electroencephalogram Signal Analysis Based on the Improved k...

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International Congress on image and Signal Processing, BioMedical Engineering and Informatics

作者： Xian Yu Chengcheng Liu Jun Wang Jiafei Dai Jin Li Fengzhen Hou Image Processing and Image Communications Key Lab Nanjing University of Posts and Telecommunications Nanjing General Hospital of Nanjing Military Command College of Physics and Information Technology Shaanxi Normal University School of Science China Pharmaceutical University

ISBN: (纸本)9781509037117

In this paper, we propose a method to analyze epileptic electroencephalogram based on time series that is transformed from improved k-nearest neighbor network. The study of complex networks has become a hot research of electroencephalogram signal. Electroencephalogram time series generated by the network keeps node information of network, so researching the time series from the network can also achieve the purpose of studying epileptic electroencephalogram. The results of this experiment show that studying power spectrum of time series from the network is more easily than the power spectrum of time series directly generated from brain data to distinguish between normal and epileptic patients. In addition, studying the clustering coefficient of improved k-nearest neighbor network is also able to distinguish between normal and patients with epilepsy. This study can provide an important reference for the study of epilepsy and clinical diagnosis.

关键词： analysis of electroencephalogram signals improved k-nearest neighbor network time series epilepsy

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Analogical China map constructed by single-stranded DNA tiles assembly

引用

Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3865-3870页

作者： Wang, Yanfeng Ma, Xin Li, Mengmeng Pan, Linqiang Cui, Guangzhao Key Lab of Information-Based Electrical Appliances Zhengzhou University of Light Industry Zhengzhou450002 China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei430074 China

In the past nearly two decades, DNA self-assembly technology as a promising technology, a body of laboratory work has been emerged in an endless stream. Single-stranded DNA tile (SST) assembly provides a simple, modular and robust framework for constructing nanostructures with prescribed shapes from short synthetic DNA strands. In this research, we present a nanoscale DNA structure of analogical China map which is constructed by a number of SSTs, and label streptavidin in the capital beijing location. The constructing nanostructure is roughly 98 nm×67 nm in diameter with a spatial resolution of 3 nm×7 nm. The pattern appeared in atomic force microscopy (AFM) conforms to the pre-designed shape of Chinese map with the approximately expected dimensions, and AFM images of the field selected randomly from the mica give an 11% assembly yield. Through this research, the capability of constructing complicated two-dimensional shapes by SST and the controllability of the internal array are fully vindicated. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Nanostructures

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Multiscale Relative Transition Entropy Analysis of Electroencephalogram

Multiscale Relative Transition Entropy Analysis of Electroen...

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Ying Wang Fengzhen Hou Jiafei Dai Jin Li Jun Wang Image Processing and Image Communications Key Lab. Nanjing Univ.of Posts & Telecomm. School of Science China Pharmaceutical University Nanjing General Hospital of Nanjing Military Command College of Physics and Information Technology Shaanxi Normal University

ISBN: (纸本)9781510823808

Recently,quantifying the complexity of the physiological time series has become more and more *** one of the most complex physiological signals,electroencephalogram(EEG) including a large number of physiological and pathological information attracts widespread ***,many traditional algorithms fail to account for the multiple times scales inherent in physiologic *** this paper,we proposed multiscale relative transition entropy algorithm(MRTE) to analyze the white noise and pink noise,the adolescent and adults EEG as well as normal and epileptic *** results indicate that there are distinct tendency among different types EEG which indicating that the multiscale relative transition entropy can distinguish different physiological and pathological signals.

关键词： multiscale relative transition entropy complexity

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Analyzing Occupational Stress Based On Inner Composition Alignment Algorithm

Analyzing Occupational Stress Based On Inner Composition Ali...

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International Congress on image and Signal Processing, BioMedical Engineering and Informatics

作者： Zhen Shi Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm China Nanjing Brain Hospital China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China

ISBN: (纸本)9781509037117

To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG) and the nurses' EEG and to explore the influence of occupational stress on EEG. First, we selected nine cases of students EEG data and the same quantity of nurses EEG data from General Hospital of Nanjing Military Region, then, constructed two different brain networks by utilizing IOTA method and analyzed the networks characteristics which include IOTA coefficient, Clustering coefficient and Average degree. In order to verify the accuracy of the results, we used SPSS to analyse the significant difference hypothesis testing. The IOTA coefficients are inconsistent between students' EEG and nurses' EEG. Compared with the students, nurses' brain network is more complex. The level of occupational stress of nurses is higher than students. The IOTA algorithm can significantly distinguish occupational stress between students and nurses, and all results demonstrated the validity of the algorithm.

关键词： Brain Network Occupational Stress EEG IOTA Complex Network

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A novel method for the construction of evolutionary tree based on expectation maximization algorithm

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Journal of Computational and Theoretical Nanoscience 2016年第6期13卷 3799-3803页

作者： Chen, Zhihua Zhao, Yang Wang, Zhuo Kou, Zheng Li, Hanggao Key Lab. of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei430074 China Institute of Biomedical and Pharmaceutical Sciences College of Biological Engineering and Food Science Hubei University of Technology Wuhan Hubei430068 China College of Computer Science South-Central University for Nationalities Wuhan Hubei430074 China

The difference of protein sequence or protein structure can be used for the construction of molecular evolutionary tree or phytogenetic tree with certain hierarchy and topology. The divergent points in the tree suggest that new genes appeared or common ancestors varied. The lengths of the branch in the tree show the evolution distances between proteins when divergences happened. The analysis of protein evolution can propose some methods for molecular evolution of various species and can confirm the accurate status in the evolution history. Moreover, the molecular evolution tree of proteins is a strong evidence for species classification. In the paper, the trees are constructed by the regular express of protein sequences and the expectation maximization (EM) algorithm. The proposed method in the paper can be used conveniently and can give reliable results, which benefit the biology computing and sequence analysis. Copyright © 2016 American Scientific Publishers. All rights reserved.

关键词： Proteins

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On the Training of DNN-based Average Voice Model for Speech Synthesis

On the Training of DNN-based Average Voice Model for Speech ...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

作者： Shan Yang Zhizheng Wu Lei Xie Shaanxi Provincial Key Lab of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China The Centre for Speech Technology Research University of Edinburgh UK

ISBN: (纸本)9781509024018

Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this work, we perform a systematic analysis of the training of multi-speaker average voice model (AVM), which is the foundation of adaptability and controllability of a DNN-based speech synthesis system. Specifically, we employ the i-vector framework to factorise the speaker specific information, which allows a variety of speakers to share all the hidden layers. And the speaker identity vector is augmented with linguistic features in the DNN input. We systematically analyse the impact of the implementations of ivectors and speaker normalisation.

关键词： Training DNN-based Average Voice Model Speech Synthesis

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