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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Esa Prakasa Bambang Sugiarto Computer Vision Research Group Research Center for Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia

ISBN: (数字)9781728189222

ISBN: (纸本)9781728189239

The pandemic situation of COVID-19 is still ongoing in every part of the world countries. Up to now, the medicine and the vaccination for curing the COVID-19 are not yet available. Since the vaccine and medication of COVID-19 are not yet available, the government in many countries are strongly socialising three kinds of new practices. The new habits are physical distancing, wearing a mask, and handwashing frequently. This paper focuses on assessing the completeness of handwashing. The handwashing frames are extracted from a video clip. The frames are then categorised into six-movement steps by using a deep learning-based algorithm. The frame of hand object is separated from the video by applying skin colour differentiation. Two kinds of experiments based on two different video sources are performed in the paper. Scoring simulation is also conducted in the research. The results show that the proposed method can give an excellent performance with accuracy values not less than 90%.

关键词： COVID-19 Image segmentation Motion segmentation Mathematical model Image color analysis Pandemics computer architecture

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Prediction of energy resolution in the JUNO experiment

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Chinese Physics C 2025年第1期49卷 35-59页

作者： Angel Abusleme Thomas Adam Kai Adamowicz Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Joao Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Marco Beretta Antonio Bergnoli Daniel Bick Lukas Bieger Svetlana Biktemerova Thilo Birkenfeld Iwan Blake David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Stéphane Callier Steven Calvez Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen Shaomin Chen Xin Chen Yiming Chen Yixue Chen Yu Chen Zelin Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng YuChin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-Ting Chin Po-Lin Chou Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla Selma Conforti Di Lorenzo Alberto Coppi Daniele Corti Simon Csakli Chenyang Cui Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La Taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding Bayu Dirgantara Carsten Dittrich Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Katherine Dugas Stefano Dusini Hongyue Duyang Jessica Eck Timo Enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Dmitry Fedoseev Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Fritsch Fritsch Haonan Gan Feng Gao Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Marco Grassi Maxim Gromov Vasily Gromov Minghao G Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi'an Jiaotong University Xi'anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

This paper presents an energy resolution study of the JUNO experiment,incorporating the latest knowledge acquired during the detector construction *** determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 *** achieve this ambitious goal,significant efforts have been undertaken in the design and production of the key components of the JUNO *** factors affecting the detection of inverse beta decay signals have an impact on the energy resolution,extending beyond the statistical fluctuations of the detected number of photons,such as the properties of the liquid scintillator,performance of photomultiplier tubes,and the energy reconstruction *** account for these effects,a full JUNO simulation and reconstruction approach is *** enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy *** results of this study reveal an energy resolution of 2.95% at 1 ***,this study assesses the contribution of major effects to the overall energy resolution *** analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data ***,it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

关键词： energy resolution liquid scintillator detector JUNO simulation reconstruction

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Three for one and one for three: Flow, Segmentation, and Surface Normals

arXiv

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arXiv 2018年

作者： Le, Hoang-An Baslamisli, Anil S. Mensink, Thomas Gevers, Theo Computer Vision Group Informatics Institute University of Amsterdam Netherlands

Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination. We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features. To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmentation, optical flow, and surface normals. The experimental results show positive influence among the three modalities, especially for objects' boundaries, region consistency, and scene structures. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical flows

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3D human action recognition for multi-view camera systems

3D human action recognition for multi-view camera systems

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission

作者： Holte, Michael B. Moeslund, Thomas B. Nikolaidis, Nikos Pitas, Ioannis Computer Vision and Media Technology Laboratory Department of Architecture Design and Media Technology Aalborg Universtity Denmark Greece Department of Informatics Center for Research and Technology Hellas Informatics and Telematics Institute Greece

ISBN: (纸本)9780769543697

This paper presents a novel approach for combining optical flow into enhanced 3D motion vector fields for human action recognition. Our approach detects motion of the actors by computing optical flow in video data captured by a multiview camera setup with an arbitrary number of views. Optical flow is estimated in each view and extended to 3D using 3D reconstructions of the actors and pixel-to-vertex correspondences. The resulting 3D optical flow for each view is combined into a 3D motion vector field by taking the significance of local motion and its reliability into account. 3D Motion Context (3D-MC) and Harmonic Motion Context (HMC) are used to represent the extracted 3D motion vector fields efficiently and in a viewinvariant manner, while considering difference in anthropometry of the actors and their movement style variations. The resulting 3D-MC and HMC descriptors are classified into a set of human actions using normalized correlation, taking into account the performing speed variations of different actors. We compare the performance of the 3D-MC and HMC descriptors, and show promising experimental results for the publicly available i3DPost Multi-View Human Action Dataset. © 2011 IEEE.

关键词： Optical flows

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Sample distillation for object detection and image classification 6

Sample distillation for object detection and image classific...

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6th Asian Conference on Machine Learning, ACML 2014

作者： Canévet, Olivier Lefakis, Leonidas Fleuret, François Computer Vision and Learning group Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have developed a "distillation" procedure to recursively reduce the size of an initial training set using a criterion that ensures the maximization of the information content of the selected sub-set. We demonstrate the performance of this procedure for two different computer vision problems. First, we show that distillation can be used to improve the traditional bootstrapping approach to object detection. Second, we apply distillation to a classification problem with artificial distortions. We show that in both cases, using the result of a distillation process instead of a random sub-set taken uniformly in the original sample set improves performance significantly. © 2014 O. Canévet, L. Lefakis & F. Fleuret.

关键词： Distillation

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Efficient sample mining for object detection 6

Efficient sample mining for object detection

引用

6th Asian Conference on Machine Learning, ACML 2014

作者： Canévet, Olivier Fleuret, François Computer Vision and Learning group Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Lausanne Switzerland

Object detectors based on the sliding window technique are usually trained in two successive steps: first, an initial classifier is trained on a population of positive samples (i.e. images of the object to detect) and negative samples randomly extracted from scenes which do not contain the object to detect. Then, the scenes are scanned with that initial classifier to enrich the initial set with negative samples incorrectly classified as positive. This bootstrapping process provides the learning algorithm with "hard" samples, which help to improve the decision boundary. Little work has been done on how to efficiently enrich the training set. While the standard bootstrapping approach densely visits the scenes, we propose to evaluate which regions of scenes can be discarded without any further computation to concentrate the search on promising areas. We apply our method to two standard object detection settings, pedestrian and face detection, and show that it provides a multi-fold speed up. © 2014 O. Canévet & F. Fleuret.

关键词： Object detection

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Fractal image compression on spiral architecture

Fractal image compression on spiral architecture

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International Conference on computer Graphics, Imaging and Visualisation, CGIV'06

作者： He, Xiangjian Wang, Huaqing Wu, Qiang Hintz, Tom Hur, Namho Computer Vision Research Group University of Technology Sydney Broadcasting System Research Group Digital Broadcasting Research Division Electronics Telecommunications Research Institute 161 Gajeong-dong Yuseong-gu Daejeon 305-700 Korea Republic of

ISBN: (纸本)0769526063

Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS's through the construction of a Complete Metric Space on SA. The selection of range and domain blocks for fractal image compression is highly related to the uniform image separation specific to SA. In this paper, we will review the current research work on fractal image compression based on SA. We will compare the results obtained on SA and the traditional square structure in terms of compression ratio and PSNR. © 2006 IEEE.

关键词： Image compression

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Application of multi-channel hough transform to stereo vision

Application of multi-channel hough transform to stereo visio...

引用

Intelligent Robots and computer vision VI 1987

作者： Nasrabadi, Nasser M. Liu, Yi Computer Vision Research Group Worcester Polytechnic Institute Department of Electrical Engineering Atwater Kent Laboratories 100 Institute Road WorcesterMA01609 United States

A feature-based stereo vision technique is described in this paper where curve-segments are used as the feature primitives in the matching process. The local characteristics of the curve-segments are extracted by the Generalized Hough Transform (R-table) representation of the curve-segment. The left image and the right image are first filtered by using several Laplacian of a Gaussian operators (V2G) of different widths. At each channel, the Generalized Hough Transform of each curve-segment in the left and the right image is evaluated. This is done by calculating the R-table representation of each curve-segment based upon the cen-troid of the curve-segment. The R-table, curve-length, and the average gradient of the curve are used as a local feature vector in representing the distinctive characteristics of the curve-segment. The feature vector of each curve-segment is used as a constraint to find an instance of the same curve-segment in the right image. The epipolar constraint on the centroids of the curve-segment is used to limit the searching space in the right image. A relational graph is formed from the left image by treating the centroids of the curve-segment as the nodes of the graph. The local features of the curve-segments are used to represent the local properties of the nodes, and the relationship between the nodes represents the structural properties of the object in the scene. A similar graph is also formed from the right image curve-segments. Sub-graph isomorphism is then formed between the two graphs by using the epipolar constraint on the centroids, the local properties of the nodes (node assignment), and the structural relationship (compatibility) between the nodes. © 1988 SPIE. All rights reserved.

关键词： Stereo vision

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Constructing optimized prototypes for nearest neighbor classifiers

Constructing optimized prototypes for nearest neighbor class...

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15th International Conference on Pattern Recognition (ICPR-2000)

作者： Huang, YS Chiang, CC Shieh, JW Grimson, E Advanced Technology Center Computer and Communications Lab. Industrial Technology Research Institute United States Computer Vision Group Artificial Intelligence Lab. Massachusetts Institute of Technology United States

ISBN: (纸本)0769507514

A novel method of constructing optimized prototypes for nearest-neighbor classifiers is proposed. Based on an effective classification oriented error function containing class classification and class separation components, the corresponding prototype and feature weight updating rules are derived. The proposed method consists of several distinguished properties. First, not only prototypes but also feature weights are constructed during the optimization process;Second, Several instead of one prototype not belonging to the genuine class of input sample x are updated when x is classified incorrectly;Third, it intrinsically distinguishes different learning contribution from training samples, which enables a large amount of learning from constructive samples, and limited learning from outlier ones. Experiments have shown the superiority of this method compared with LVQ2 and other previous work [1].

关键词： Speech recognition

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