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8th International Conference on Digital Home, ICDH 2020

作者： Huang, Wenming Zhang, Juan Xiao, Yannan Han, Zheng Deng, Zhenrong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin China Guilin Bank Guilin China

ISBN: (纸本)9781728192345

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field. © 2020 IEEE.

关键词： Semantics

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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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A Multi-Level Thresholding image Segmentation Based on an Improved Artificial Bee Colony Algorithm 2nd

A Multi-Level Thresholding Image Segmentation Based on an Im...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： image segmentation

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Model-data co-driven U-net segmentation network for multimodal lung tumor images

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Applied Soft Computing 2025年 180卷

作者： Tao Zhou Pei Dang Yunfeng Pan Caiyue Peng Qitao Liu Huiling Lu Fuyuan Hu School of Computer Science and Engineering North Minzu University Yinchuan 750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan 750021 China School of Medical information and Engineering Ningxia Medical University Yinchuan 750004 China School of Electronic& Information Engineering Suzhou University of Science and Technology Suzhou 215009 China

There are some challenges in multimodal medical image segmentation. Based on this, the Model-Data Co-driven U-Net Segmentation Network for Multimodal Lung Tumor images is proposed. About “How to extract edge features in CT image?”, Edge-Driven U-Net is designed, which includes CT Data Stream Edge-Driven Module. The model ability to perceive lesion edge features is improved. About “How to extract position features in PET image?”, Position-Driven U-Net is designed, which includes PET Data Stream Position-Driven Module. The model ability to perceive lesion position features is improved. About “How to extract content features in PET/CT image?”, Content-Driven U-Net is designed, which includes PET/CT Data Stream Content-Driven Module. Under the guidance of semantic information in deep features, the model ability to perceive lesion content features is improved. About “How to fuse image features of PET/CT, PET and CT images?”, Model-Data Co-driven Module is designed. The main work of this model are as following: Firstly, the Shallow Model-Data Co-driven Module is designed, which realizes the interactive learning of different model data streams. Secondly, the Deep Model-Data Co-driven Module is designed, which realizes the interactive guide to learn position, edge and content features. The effectiveness of Model-Data Co-driven U-Net Segmentation Network for Multimodal Lung Tumor images is validated on a clinical multimodal lung medical image dataset. The results for Miou, Dice, Voe, Rvd, Acc, and Recall are 91.60 %，95.94 %，95.92 %，95.73 %，97.92 and 95.28 % in lung lesion segmentation. The method proposed in this paper has positive significance for computer-aided diagnosis.

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Multi-Wavelength Narrow Linewidth Random Fiber Laser Based on Fiber Bragg Grating Fabry-Perot Filter

Multi-Wavelength Narrow Linewidth Random Fiber Laser Based o...

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International Conference on Information Science and Technology (ICIST)

作者： Sokliep Pheng Luo Xiaonan Zhongshuai Wang Yijie Zhu Zetao Jiang School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728155586

ISBN: (纸本)9781728155593

Recently, multi-wavelength narrow linewidth random fiber laser has very interested for every researcher in this field, because of their useful advantages application, such as high-resolution spectroscopy and fiber optic sensing. In this paper, the standard single-mode fiber is used to form a half-opened cavity structure for generating the narrow linewidth random fiber laser and used the FBG-FP as a filter to form the narrow linewidth RFL into multi-wavelength. Firstly, we used the Rayleigh scattering that processed as well as in a standard single-mode fiber to provide random distribution feedback at the same time while using erbium-doped fiber (EDF) to provide the gain or amplification for achieving a broadband random laser output. Then, FBG-FP is added to the half-open cavity random laser structure. The multi-wavelength and narrow linewidth RFL can be achieved when the broadband RFL goes through the FBG-FP. In this paper we have generated the multi-wavelength narrow linewidth random fiber laser which has more than 10 wavelengths and the 3dB bandwidth is less than 0.01 nm and the mode separation of each wavelength is 0.04nm.

关键词： Fiber gratings Erbium-doped fiber lasers Optical fiber couplers Optical fiber amplifiers

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Rail surface defects detection based on gray scale gradient characteristics of image

引用

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2018年第4期39卷 220-229页

作者： Min, Yongzhi Yue, Biao Ma, Hongfeng Xiao, Benyu School of Automation and Electrical Engineering Lanzhou Jiao tong University Lanzhou730070 China Rail Transit Electrical Automation Engineering Laboratory of Gansu Province Lanzhou Jiaotong University Lanzhou730070 China Gansu Provincial Engineering Research Center for Artifical Intelligence and Graphics & Image Processing Lanzhou730070 China School of Electronic Information Engineering Lanzhou Institute of Technology Lanzhou730050 China

When the machine vision technology is adopted to detect rail surface defects, the accuracy of results is easily influenced by the change of illumination, uneven reflection of rail surface, rust, etc. Therefore, this paper proposes a detection method for rail surface defection based on gray scale gradient characteristics of image. Firstly, the gray scale and gradient characteristics of different regions in the rail image are analyzed with the proposed defects detection device. Then, the background smoothing filter is designed based on the idea of bilateral filtering. By using local gray scale and gradient change information to adjust the smoothing degrees of different feature region adaptively, the original image is smoothed to generate a background image. Finally, image difference is made. The detection of rail surface defects is realized by setting threshold for the differential image and using connected-component labeling method. The experimental results show that the defects region in the image can be highlighted. Meanwhile, the proposed method can reduce the impact of illumination change, uneven reflection of rail surface, rust, etc. Both scar and crack defects in different track environments have been detected effectively. The undetected rate and false detection rate are 5.79% and 6.84%, respectively, which mean that the proposed method has certain practical value. © 2018, Science Press. All right reserved.

关键词： Surface defects

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image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication

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Efficient MedSAMs: Segment Anything in Medical images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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An extended probabilistic collaborative representation based classifier for image classification

An extended probabilistic collaborative representation based...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Rushi Lan Yicong Zhou Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China Department of Computer and Information Science University of Macau Macau China

ISBN: (纸本)9781509060689

Collaborative representation based classifier (CRC) and its probabilistic improvement ProCRC have achieved satisfactory performance in many image classification applications. They, however, do not comprehensively take account of the structure characteristics of the training samples. In this paper, we present an extended probabilistic collaborative representation based classifier (EProCRC) for image classification. Compared with CRC and ProCRC, the proposed EProCRC further considers a prior information that describes the distribution of each class in the training data. This prior information enlarges the margin between different classes to enhance the discriminative capacity of EProCRC. Experiments on two challenging databases, namely CUB200-2011 and Caltech-256, are conducted to evaluate EProCRC, and comparison results demonstrate that it outperforms several state-of-the-art classifiers.

关键词： Training Collaboration Probabilistic logic Training data Feature extraction Databases Robustness

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A Temporally and Spatially Explicit, Mechanistic Estimation of Ragweed Pollen Concentrations Across Europe

SSRN

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SSRN 2023年

作者： Makra, László Matyasovszky, István Tusnády, Gábor Ziska, Lewis H. Hess, Jeremy J. Nyúl, László G. Chapman, Daniel S. Coviello, Luca Gobbi, Andrea Jurman, Giuseppe Furlanello, Cesare Brunato, Mauro Damialis, Athanasios Charalampopoulos, Athanasios Müller-Schärer, Heinz Schneider, Norbert Szabó, Bence Sümeghy, Zoltán Páldy, Anna Magyar, Donát Bergmann, Karl-Christian Deák, Áron József Mikó, Edit Thibaudon, Michel Oliver, Gilles Albertini, Roberto Bonini, Maira Šikoparija, Branko Radišić, Predrag Mitrović Josipović, Mirjana Gehrig, Regula Severova, Elena Rodinkova, Victoria Palamarchuk, Olena Stjepanović, Barbara Ianovici, Nicoleta Berger, Uwe Kofol Seliger, Andreja Rybníček, Ondřej Myszkowska, Dorota Dąbrowska-Zapart, Katarzyna Majkowska-Wojciechowska, Barbara Weryszko-Chmielewska, Elzbieta Grewling, Lukasz Rapiejko, Piotr Malkiewicz, Malgorzata Šaulienė, Ingrida Shalaboda, Valentina Prykhodo, Olexander Maleeva, Anna Ščevková, Jana Bullock, James M. Institute of Economics and Rural Development Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Department of Meteorology Eötvös Loránd University P.O.B. 32 Budapest1518 Hungary Alfréd Rényi Institute of Mathematics P.O.B 127 Budapest1364 Hungary Mailman School of Public Health Columbia University New YorkNY10032 United States Department of Global Health University of Washington SeattleWA98105 United States Department of Image Processing and Computer Graphics University of Szeged P.O.B. 652 Szeged6701 Hungary Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Scotland StirlingFK9 4LA United Kingdom University of Trento Enogis s.r.l. Trento Italy Bruno Kessler Foundation Trento Italy HK3 Lab Orobix Life Srl Rovereto Bergamo Italy Department of Information Engineering and Computer Science University of Trento Trento Italy Terrestrial Ecology and Climate Change Department of Ecology School of Biology Aristotle University of Thessaloniki ThessalonikiGR-54124 Greece Departement of Biology Unit of Ecology and Evolution University of Fribourg FribourgCH-1700 Switzerland National Institute of Environmental Health Albert Flórián út 2-6 Budapest1097 Hungary Institute of Allergology Charité Hindenburgdamm 30 Berlin12203 Germany Institute of Animal Science and Wildlife Management Faculty of Agriculture University of Szeged Andrássy út 15 Hódmezővásárhely6800 Hungary Réseau National de Surveillance Aérobiologique 11 chemin de la Creuzille Le Plat du Pin Brussieu696905 France Laboratory of Hygiene and Aerobiology Department of Medicine and Surgery University of Parma U.O. Medicina Interna di Continuità Azienda Ospedaliero-Universitaria di Parma Via Gramsci 14 Parma43126 Italy Hygiene and Public Health Service via Spagliardi 19 Parabiago Milan20015 Italy BioSensе Institute Research Institute for Information Technologies in Biosystems University of Novi Sad Dr

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant’s reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating annual total pollen concentrations than those of the GM approach. These are the first published modeled maps using altitude correction and phenology to recover missing pollen information. We created a web page (http://***/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions. © 2023, The Authors. All rights reserved.

关键词： Climate change

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