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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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Research on Palm Vein Recognition Algorithm Based on Improved Convolutional Neural Network

Research on Palm Vein Recognition Algorithm Based on Improve...

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International Automatic Control Conference

作者： Bo Sun Xunfang Tao Ji li Xiaonan Luo Guangxi Key Laboratory of Intelligent processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

When collecting palm vein images, it is easy to be affected by external factors such as light source and placement angle, which result in poor recognition accuracy. In this paper, a new method, which involve a new method of region of interest segmentation and an improved palm recognition method of VGG16 deep convolutional neural network, was proposed to promote the recognition accuracy and be well adapted to the actual application scenarios. Firstly, the original palm vein image is obtained through profile original image, positioning key point of original image, and extract region of interest image. Afterwards, the adaptive histogram equalization technique and Gaussian Filters are utilized to improve image quality. Secondly, for palm vein image recognition application scenarios, the output of the convolutional layer of the VGG-16 convolutional neural network is standardized in batches, and the attention mechanism is introduced to optimize the VGG-16 neural network. The optimized network is used for feature extraction and recognition of palm vein images. Thirdly, data enhancement was performed on the public Polyu multispectral palm vein data set, and then a large number of experiments were carried out, and the best recognition rate was 99.57%.

关键词： Image segmentation Image recognition Veins Neural networks Feature extraction Convolutional neural networks Light sources

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A Novel Biometric Method for Blurred Palm Vein images

A Novel Biometric Method for Blurred Palm Vein Images

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International Automatic Control Conference

作者： Xunfang Tao Bo Sun Ji Li Xiaonan Luo Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728171982

ISBN: (纸本)9781728171999

This paper proposes a palm vein recognition system based on compressed sensing theory to solve the problem of blurred vein image recognition, and establishes a more accurate palm vein recognition system. This method is mainly composed of two parts: First, the compressed sensing is utilized to reconstruct the palm vein image extracted by the infrared sensor to obtain a clearer image than the original blurred image. Second, geometry-based method for processing palm vein images to extract image feature information easier. This paper solve the problems of blurred veins, difficult segmentation and feature information extraction in palm vein images. The accurate recognition experiment of palm vein images is conducted on the polyu database which processed by gaussian noise show that our method has advantages in accuracy compared to traditional palm vein recognition systems.

关键词： Image recognition Databases Veins Feature extraction Security Compressed sensing Image reconstruction

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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： Zhou, Tao Dang, Pei Pan, Yunfeng Peng, Caiyue Liu, Qitao Lu, Huiling Hu, Fuyuan School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical information and Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China School of Electronic& Information Engineering Suzhou University of Science and Technology Suzhou215009 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. © 2025

关键词： Semantic Segmentation

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

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

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Design of intelligent Robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

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