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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： Image segmentation

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An effective coaxiality measurement for twist drill based on line structured light sensor

arXiv

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arXiv 2021年

作者： Cheng, Ailing Ye, Jiaojiao Yang, Fei Lu, Shufang Gao, Fei The Laboratory of Graphics & Image Processing College of Computer Science and Technology Zhejiang University of Technology Zhejiang Province Hangzhou310023 China The Research Center of Intelligent Computing Software Zhejiang Lab. Zhejiang Province Hangzhou311121 China

Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four core units is designed, which can obtain the 3-D point cloud data of full angle for the twist drill. Second, in the data processing stage, an improved robust Gaussian mixture model is established for accurate and rapid blade back segmentation. To improve measurement efficiency, a rapid reconstruction method of the twist drill axis based on orthogonal synthesis is provided to locate the axial position of the maximum deviation from the benchmark by utilizing the extracted blade back data. Finally, by calculating the maximum radial Euclidean distance from the benchmark, the coaxiality error of the twist drill is obtained. Comparing with other measurement methods, experimental results show that our proposed method is effective with high precision of 3 um and high efficiency of less than 3 s/pc. The result demonstrate that the proposed method is effective, robust and automatic, it can be applied in many actual industrial scene. © 2021, CC BY.

关键词： Drills

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

Design of People Flow Monitoring System in Public Place base...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Tianmin, Xiong Xiaochun, Lei Xingwen, Zheng Junyan, Chen Yizhou, Feng School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Design of Intelligent Robot Platform based on Multi-sensor Fusion

Design of Intelligent Robot Platform based on Multi-sensor F...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Dajin, Ya Xiaochun, Lei Yue, Li Junyan, Chen Rongcun, Huang Lin, Lan School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Navigation

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Memorizing all for implicit discourse relation recognition

arXiv

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arXiv 2019年

作者： Bai, Hongxiao Zhao, Hai Zhao, Junhan Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Computer Graphics Technology Purdue University West LafayetteIN United States

Implicit discourse relation recognition is a challenging task due to the absence of the nec-essary informative clue from explicit connec-tives. The prediction of relations requires a deep understanding of the semantic meanings of sentence pairs. As implicit discourse relation recognizer has to carefully tackle the se-mantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data. Thus in this paper, we propose a novel memory mecha-nism to tackle the challenges for further performance improvement. The memory mecha-nism is adequately memorizing information by pairing representations and discourse relations of all training instances, which right fills the slot of the data-hungry issue in the current im-plicit discourse relation recognizer. Our experiments show that our full model with memo-rizing the entire training set reaches new state-of-the-art against strong baselines, which es-pecially for the first time exceeds the milestone of 60% accuracy in the 4-way task. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Sparse weighted naive bayes classifier for efficient classification of categorical data 3

Sparse weighted naive bayes classifier for efficient classif...

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Zheng, Zhuoyuan Cai, Yunpeng Yang, Yujie Li, Ye Guangxi Key Laboratory of Trusted Software Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin541004 China Shenzhen Institutes of Advanced Technology Key Laboratory for Biomedical Informatics and Health Engineering Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781538642108

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE. © 2018 IEEE.

关键词： Feature Selection

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Underwater photogrammetry in very shallow waters: Main challenges and caustics effect removal

Underwater photogrammetry in very shallow waters: Main chall...

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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Agrafiotis, P. Skarlatos, D. Forbes, T. Poullis, C. Skamantzari, M. Georgopoulos, A. Cyprus University of Technology Civil Engineering and Geomatics Dept. Lab of Photogrammetric Vision 2-8 Saripolou str. Limassol3036 Cyprus Immersive and Creative Technologies Lab. Department of Computer Science and Software Engineering Concordia University QuebecH3G 1M8 Canada National Technical University of Athens School of Rural and Surveying Engineering Lab. of Photogrammetry Zografou Campus 9 Heroon Polytechniou str. Zografou Athens15780 Greece

In this paper, main challenges of underwater photogrammetry in shallow waters are described and analysed. The very short camera to object distance in such cases, as well as buoyancy issues, wave effects and turbidity of the waters are challenges to be resolved. Additionally, the major challenge of all, caustics, is addressed by a new approach for caustics removal (Forbes et al., 2018) which is applied in order to investigate its performance in terms of SfM-MVS and 3D reconstruction results. In the proposed approach the complex problem of removing caustics effects is addressed by classifying and then removing them from the images. We propose and test a novel solution based on two small and easily trainable Convolutional Neural Networks (CNNs). Real ground truth for caustics is not easily availab.e. We show how a small set of synthetic data can be used to train the network and later transfer the le arning to real data with robustness to intra-class variation. The proposed solution results in caustic-free images which can be further used for other tasks as may be needed. © Authors 2018.

关键词： Underwater photogrammetry

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Constructing Multiple Scene Graphs in Distributed Environment

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computer Aided Drafting,Design and Manufacturing 2015年第1期25卷 16-21页

作者： XIA Rui WANG Guo-ping LI Sheng WANG Heng Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School of Peking University Graphics & Interaction Lab. School of Electronics Engineering and Computer SciencesPeking University Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University

Scene graph is a infrastructure of the virtual reality system to organize the virtual scene with abstraction, it can provide facility for the rendering engine and should be integrated effectively on demand into a real-time system, where a large quantities of scene objects and resources can be manipulated and managed with high flexibility and reliability. We present a new scheme of multiple scene graphs to accommodate the features of rendering engine and distributed systems. Based upon that, some other functions, e.g. block query, interactive editing, permission management, instance response, ＂redo＂ and ＂undo＂, are implemented to satisfy various requirements. At the same time, our design has compatibility to popular C/S architecture with good concurrent performance. Above all, it is convenient to be used for further development. The results of experiments including responding time demonstrate its good performance.

关键词： multiple scene graphs authority scene editing distributed systems

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Preface

Proceedings of the 11th European Conference on Games Based L...

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Proceedings of the 11th European Conference on Games Based Learning, ECGBL 2017 2017年 viii-x页

作者： Pivec, Maja Gründler, Josef Admiraal, Wilfried Alemi, Minoo All, Anissa Almeida, Ana Amresh, Ashish Arnab, Sylvester Ask, Kristine Avouris, Nikolaos Awwal, Nafisa Hauge, Jannicke Baalsrud Badau, Dana Barata, Ana Barendregt, Wolmet Basaiawmoit, Rajiv Bellotti, Francesco Blanchfield, Peter Borit, Melania Boskic, Natasha Boyle, Liz Brady, Mairead Bratitsis, Tharrenos Brinch, Sara Brom, Cyril Brooks, Anthony Brown, David Brox, Ellen Busch, Carsten Caldeira, Carlos Callies, Sophie Cardoso, Tiago Carron, Thibault Casimir, Rommert De Castro, António Vieira Chang, Maiga Charalambis, Dimitris Charles, Darryl Charlier, Nathalie Chee, Yam San Chen, Ming-Puu Chiarello, Fabio Connolly, Thomas Crockett, Larry Darwish, Tamer Sigurdardottir, Helga Deliyannis, Ioannis Demirbilek, Muhammet Sigurdardottir, Helga Dis Duin, Heiko Dyer, Ronald Edgar, David Ejsing-Duun, Stine Escudeiro, Paula Felicia, Patrick Fesakis, Georgios Figueira, Ana Paula Fotaris, Panagiotis Fujimoto, Toru George, Sebastien Georgiadis, Panagiotis Gerodetti, Natalia Giannakoulopoulos, Andreas Gjedde, Lisa Goebel, Stefan Gomez-Martin, Pedro Pablo Gouscos, Dimitris Grigoriadou, Maria Guralnick, David Guttormsgaard, Vibeke Habgood, Jacob Hainey, O. Hanghoj, Thorkild Hawlitschek, Anja Hemben, Terseer Hlavacs, Helmut Hommedal, Silje Hole Hollins, Paul Idrus, Rozhan Wang, Alf Inge Ismail, Marina Jans, Ruben Juzeleniene, Saule Kalavassis, Fragiskos Kalogiannakis, Michail Karakostas, Anastasios Katzlinger-Felhofer, Elisabeth Ketamo, Harri Ketelhut, Diane Jass Kickmeier-Rust, Michael Kiili, Kristian Koeppen, Veit Kolås, Line Kordaki, Maria Korkut, Safak Kourti, Evangelia Li, Ken Lukosch, Heide MaCleod, Hamish Magnussen, Rikke Majgaard, Gunver Marchetti, Emanuela Marques, Bertil Marty, Jean-Charles Marzantowicz, Lukasz Mehm, Florian Meimaris, Michail Michel, Christine Moffat, David Montero-Fleta, Begoña Moseley, Alexander Mota, Dulce Mozelius, Peter Nadolski, Rob Nelson, Brian Neville, Karen Ng, Annie W.Y. Nixon, Darren Nocchi, Susanna Broin, Daire O. O'Mullane, John Skev Leiden University Leiden Netherlands Sharif University of Technology Iran IMinds-MICT-Ghent University Belgi Belgium Universidade de Coimbra Portugal Arizona State University United States Coventry University United Kingdom Norway University of Patras Greece University of Melbourne Australia Bremer Institut for Produktion und Logistik Germany University of Medicine and Pharmacy of Targu Mures Romania Portugal Gothenburg University Department of Applied IT Sweden Aarhus University Denmark University of Genoa Italy School of Computer Science University of Nottingham United Kingdom UiT - The Arctic Univeristy of Norway University of Troms Norway University of British Columbia Canada Univesity of the West of Scotland United Kingdom Open University of Cyprus Greece ESCP Europe France Trinity College Dublin Ireland University of Western Macedonia Greece KDU University College Malaysia Charles University Prague Czech Republic Aalborg University Denmark Nottingham Trent University United Kingdom Nord-Trøndelag University College Steinkjer Norway University of Applied Sciences HTW-Berlin Germany University of Evora Portugal UQAM Canada Universidade Nova de Lisboa Portugal LIP6 - Paris Université de Savoie France Tilburg University Netherlands Open University of the Natherlands Nederland United States Athabasca University Canada University of Athens Greece University of Ulster United Kingdom Catholic University of Leuven Belgium Nanyang Technological University Singapore Singapore National Taiwan Normal University Taiwan Italy University of West of Scotland United Kingdom Augsburg College United States Brunel University United Kingdom Ionian University Greece Suleyman Demirel University Turkey Glamorgan University United Kingdom BIBA - Bremer Institut for Produktion und Logistik GmbH Germany Managment School University of Liverpool United Kingdom Glasgow Caledonian University United Kingdom Aalborg Univ. Copenhagen Denmark Portugal Waterford Institute of

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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