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Distribution based EEG baseline classification 10

Distribution based EEG baseline classification

引用

10th Indian conference on computer vision, graphics and Image Processing, ICVGIP 2016

作者： Gopika Gopan, K. Sinha, Neelam Dinesh Babu, J. International Institute of Information Technology Bangalore Bangalore India

Electrical signals generated in the brain, known as Electroen-cephalographic (EEG) signals, form a non-invasive measure of brain functioning. Baseline states of EEG are Eyes Open (EO) and Eyes Closed (EC) relaxed states. the choice of baseline used in an experiment is of critical importance since they form a reference with which other states are measured. In Brain Machine Interface, it is imperative that the system should be able to distinguish between these states and hence the need for automated classification of EEG baselines. In the proposed method, Statistical Moments are utilized. the Moment Generating Functions (MGFs) obtained using these moments are given as features to SVM and k-NN classifiers resulting in mean accuracies of 86.71% and 86.54%. the fact that MGF is able to differentiate between these states indicate that the two states have different source distribution parameters. A Smirnov test verified that the data of two classes indeed come from different distributions. © Springer international Publishing AG 2017.

关键词： Brain computer interface

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Deep Learning-based Overlapping-Pig Separation by Balancing Accuracy and Execution Time 27

Deep Learning-based Overlapping-Pig Separation by Balancing ...

引用

27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019

作者： Lee, Hanhaesol Sa, Jaewon Chung, Yongwha Park, Daihee Kim, Hakjae Dept. of Computer Convergence Software Korea University Sejong30019 Korea Republic of Class Act Co. Ltd. Digital-ro Geumcheon-gu Seoul08589 Korea Republic of

ISBN: (纸本)9788086943374

the crowded environment of a pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a top-view camera. Although it is required to correctly separate overlapping-pigs for tracking each individual pigs, extracting the boundaries of each pig fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate overlapping-pigs not only by exploiting the advantage (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the disadvantage (i.e., the axis aligned bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with the occlusion patterns between the overlapping-pigs show that the proposed method can provide better accuracy and faster processing speed than one of the state-of-the-art deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics). © 2019 University of West Bohemia. All rights reserved.

关键词： Separation

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Anatomy-Focused Volume Line Integral Convolution for Brain White Matter Visualization 27

Anatomy-Focused Volume Line Integral Convolution for Brain W...

引用

27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019

作者： Schult, T. Klose, U. Hauser, T.-K. Ehricke, H.-H. Institute for Applied Computer Science Stralsund University of Applied Sciences Zur Schwedenschanze 15 Germany MV Stralsund18435 Germany Department of Diagnostic and Interventional Neuroradiology University Hospital Tübingen Hoppe-Seyler-Straße 3 Germany BW Tübingen72076 Germany

ISBN: (纸本)9788086943374

3D visualization of volumetric line integral convolution (LIC) datasets has been a field of constant research. So far, most approaches have focused on finding suitable transfer functions and defining appropriate clipping strategies in order to solve the problem of occlusion. In medicine, extensions of the LIC algorithm to diffusion weighted magnetic resonance imaging (dwMRI) have been proposed, allowing highly resolved LIC volumes to be generated. these are used for brain white matter visualization by LIC slice images, depicting fiber structures with good contrast. However, 3D visualization of fiber pathways by volume rendering faces the problem of occlusion of anatomic regions of interest by the dense brain white matter pattern. In this paper, we introduce an anatomy-focused LIC algorithm, which allows specific fiber architectures to be visualized by volume rendering. It uses an anatomical atlas, matched to the dwMRI dataset, during the generation of the LIC noise input pattern. thus, anatomic fiber structures of interest are emphasized, while surrounding fiber tissue is thinned out and its opacity is modulated. Additionally, we present an adaptation of the orientation-dependent transparency rendering algorithm, which recently has been proposed for fiber streamline visualization, to LIC data. the novel methods are evaluated by application to dwMRI datasets from glioma patients, visualizing fiber structures of interest in the vicinity of the lesion. © 2019 University of West Bohemia. All rights reserved.

关键词： Magnetic resonance imaging

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Load Balancing Optimization for Transformer in Distributed Environment 27

Load Balancing Optimization for Transformer in Distributed E...

引用

27th IEEE international conference on Parallel and Distributed Systems, ICPADS 2021

作者： Ma, Delu Lei, Zhou Chen, Shengbo Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665408783

In recent years, the demand for artificial intelligence applications has increased dramatically. Complex models can promote machine learning to achieve excellent results, but computing efficiency has gradually reached a bottleneck. therefore, more researchers are exploring the improvement of the efficiency of intelligent computing systems. Distributed machine learning can improve the efficiency of model training and inference, but problems such as communication delay and load imbalance between computing nodes still exist. In the multi-GPU distributed computing environment, this paper takes the vision field algorithm VIT (vision transformer) as the optimization object, which has the advantage of convenient parallel training, and proposes several related solutions. Firstly, the parameter server is used as the system logic architecture and in order to reduce the idleness of the computing devices during the training process, the device working status query mechanism is designed to realize load balancing. Secondly, combined with the pre-trained small VIT algorithm model, semi-asynchronous communication method is proposed to reduce the communication overhead of computing devices and accelerate global convergence. the results of this experiment carried out in the existing distributed environment has demonstrated that compared with the existing synchronization method, the computational efficiency has been improved well under the premise of slightly reducing the accuracy. © 2021 IEEE.

关键词： Computation theory

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Adaptive Convolutional Neural Network Structure for Network Traffic Classification 27

Adaptive Convolutional Neural Network Structure for Network ...

引用

27th IEEE international conference on Parallel and Distributed Systems, ICPADS 2021

作者： Han, Zhuang Guan, Jianfeng Yao, Yanan Yao, Su School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9781665408783

Network traffic classification has been highly concerned by academia and industry for decades. In recent years, deep learning has attracted many scholars to use it in network traffic classification due to its excellent performance in the fields of computer vision and natural language processing. However, the performance of the neural network depends on its structure in the same dataset. When looking for the neural network to classify network traffic, it is necessary to constantly adjust the structure of the neural network to achieve better results, which is very time-consuming and experience-dependent. To solve the above problem, this paper proposes an Adaptive Convolutional Neural Network Structure for Network Traffic Classification (ACNNS-NTC) algorithm. the proposed algorithm first pre-processes the network traffic data used for training and testing, and then uses particle swarm optimization algorithm to optimize the network structure of the convolutional neural network, to generate convolutional neural network structure for network traffic classification, and verify the classification results. Experimental results show that the accuracies of the ACNNS-NTC algorithm on public datasets (ISCX-IDS2012, USTC-TFC2016, CIC-IDS2017) are above 99%. At the same time, the generated convolutional neural network has a more succinct structure and fewer model parameters compared with the existing methods. © 2021 IEEE.

关键词： Convolutional neural networks

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Academic and Administrative Tandems in European Projects

Academic and Administrative Tandems in European Projects

引用

EAEEIE Annual conference,

作者： Herma Adema-Labille Mathilde Labrégère Margaux Tillocher David Fofi VIBOT – International Programme in Vision and Robotics Centre Universitaire Condorcet Le Creusot France Le2i FRE2005 CNRS Arts et Métiers Univ. Bourgogne Franche Comté Le Creusot France

ISBN: (纸本)9781728109084

Building and maintaining the sustainability of European projects requires advanced communication and team investment, shared working methods, common decisions and symbiosis in key items such as selection requirement and process, terms of mobility between consortium members or teaching content. It requires jointness in various perspectives, aspects and scales, with their own conditions and challenges. this abstract describes this prerequisite of jointness through the local campus of Le Creusot (University of Burgundy) for our international Programme in computer vision and Robotics (VIBOT). the main challenge can be summarized in one single question: How to maintain and improve our level of jointness beyond the European framework of an Erasmus Mundus label?

关键词： Europe Education Collaboration Companies Sustainable development computer vision

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Medical Image Computing and computer Assisted Intervention – MICCAI 2024 Workshops 1

Medical Image Computing and Computer Assisted Intervention ...

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Anna Schroder Neil P. Oxtoby Alexandra Young Tiantian He Isaac Llorente-Saguer Xiang Li Tanveer Syeda-Mahmood Alessa Hering Tejas S. Mathai Pritam Mukherjee Sven Kuckertz Andreas Maier Satyananda Kashyap Hayit Greenspan Anant Madabhushi

ISBN: (数字)9783031845253

ISBN: (纸本)9783031845246

this book constitutes the proceedings from the workshops LDTM 2024, MMMI/ML4MHD 2024, and ML-CDS 2024 which were held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024. the papers included in this book stem from the following workshops: - LDTM 2024, Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, which accepted 13 papers from 15 submissions. - MMMI/ML4MHD 2024, the 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, and the First Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024, from which 8 papers are included from a total of 14 submissions to the workshop - ML-CDS 2024, Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, which accepted 4 papers out of 5 submissions

关键词： computer Imaging, vision, Pattern Recognition and graphics

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Data loss in quantum image representation methods 27

Data loss in quantum image representation methods

引用

27th international conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Werner, Krzysztof Kordasz, Michal Michalczuk, Agnieszka Cyran, Krzysztof Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice44-100 Poland

Quantum Image Representation is a well-studied area in quantum informatics. the first proposed algorithm was the qubit lattice method introduced in 2003. this method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. the most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： FRQI LPIQE NEQR PDU quantum computing Quantum image encoding quantum information theory quantum sampling

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Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data 1

Supervised and Semi-supervised Multi-structure Segmentation ...

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Yaqi Wang Dahong Qian Shuai Wang Achraf Ben-Hamadou Sergi Pujades Luca Lumetti Federico Bolelli Costantino Grana

ISBN: (数字)9783031889776

ISBN: (纸本)9783031889769

this book constitutes three challenges that were held in conjunction with the 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024: ToothFairy challenge(ToothFairy2: Multi-Structure Segmentation in CBCT Volumes), Semi-supervised Teeth Segmentation (STS 2024), and the 3DTeethLand (3D Teeth Landmarks Detection Challenge). the 21 papers presented in this volume were carefully reviewed and selected from 28 submissions. ToothFairy challenges focused on the development of deep learning frameworks to segment anatomical structures in CBCTs by incrementally extending the amount of publicly available 3D-annotated CBCT scans and providing the first publicly available fully annotated datasets. the STS Challenge promoted the development of teeth segmentation in panoramic X-ray images and CBCT scans. It also provided instance annotations for different teeth, including pertinent category information. the 3DTeethLand24 Challenge played a key role in advancing automation and leveraging AI to optimize orthodontic treatments. It also aims to tackle the challenge of limited access to data, providing a valuable resource that encourages community engagement in this vital area with potential clinical implications.

关键词： computer Imaging, vision, Pattern Recognition and graphics Computing Milieux computer Applications Machine Learning

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Illuminate Low-Light Image via Coarse-to-fine Multi-level Network 27th

Illuminate Low-Light Image via Coarse-to-fine Multi-level Ne...

引用

27th international conference on MultiMedia Modeling, MMM 2021

作者： Qiu, Yansheng Chen, Jun Wang, Xiao Jang, Kui National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

ISBN: (纸本)9783030678319

Images under low light or against light are of low readability and visibility which in turn cause performance degradation of many computer vision tasks. As the crucial research objects for low-brightness image enhancement, reflectance as an invariant has color information, and illuminance as a variable has brightness information. Existing technologies prefer to represent the reflectance and illumination in the same network depth directly, which ignores the internal discrepancy and correlation of these two components, further causing the details vanished and artifacts noises. Consequently, we propose coarse-to-fine multi-level decouple and fusion network (CMDFN) to decompose the restoration tasks into two sub-tasks, and use decouple network (CMDN) to learn the joint features of reflectance and illumination at different network depths. To refine the joint representation and revise color information, fusion network (CMFN) is applied to promote the illuminations map by extracting the complementary and redundant information from the reflectance map. Finally, we integrate the refined illumination and reflectance to reproduce the predicted image. Especially, in CMFN, we introduce a process based on discriminate attention mechanism to balance the noise. Experimental results have demonstrated that our proposed network is superior other state of the art methods, both visually and quantitatively. © 2021, Springer Nature Switzerland AG.

关键词： Reflection

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