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arXiv 2022年

作者： Lei, Xiaochun Mai, Weiliang Xie, Junlin Liu, He Jiang, Zetao Gong, Zhaoting Lu, Chang Lu, Linjun School of Computer Science and Information Security Guilin University of Electronic Technology Jinji Road Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Jinji Road Guangxi Guilin541004 China

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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The Research of the Accessibility and Transitivity of Topological Group

The Research of the Accessibility and Transitivity of Topolo...

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International Conference on Virtual Reality and intelligent Systems (ICVRIS)

作者： Ji Zhan-Jiang Shi Wei School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Wuzhou Vocational College China

The accessibility and transitivity have an important significance in terms of theory and application. The concept of the accessibility and transitivity are introduced in this paper. We study their dynamical properties. The following result are obtained: (l)Let (K , G ) be the hyperspace of (X , G ). Then ( X , G ) is accessible if and only if (K , G ) is accessible; (2) Let (K , G) be the hyperspace of (X , G). Then (X , G ) is transitive if and only if (K , G ) is transitive. These results enriched the theory of the accessibility and transitivity of topological group.

关键词： Virtual reality intelligent systems

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Visual Semantic Enhancement for Multi-Modal Named Entity Recognition

SSRN

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SSRN 2024年

作者： Wang, Tong Wang, Hairong Xu, Xi Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

In recent years, a series of methods have been proposed to use image semantics to assist in extracting named entities. However, in these multi-modal named entity recognition methods, there are problems of visual semantic missing and weak semantic constraints in multi-modal representation. Therefore, a visual semantic enhancement for multi-modal named entity recognition (EVS-MNER) method is proposed. In this method, multiple visual features are used for collaborative representation to obtain more complete visual semantics. The semantic information in multiple visual features is integrated through the multi-modal features fusion module to generate visual semantic features, and the semantics are passed to the text features to obtain semantically enhanced multi-modal text representation. The visual entity classifier is used to decode visual semantic features, and the semantic consistency constraint of visual features is realized. The multi-task label decoder is called to mine the fine-grained semantics of the multi-modal text representation and text features, and the problem of semantic bias is solved by joint decoding, to further improve the accuracy of named entity recognition. On two multimodal entity recognition datasets of Twitter-2015 and Twitter-2017, the proposed method is compared with 10 methods such as PCEN and M3S. The experimental results show that the proposed method is effective. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Improved Height Estimation Using Extended Kalman Filter on UWB-Barometer 3D Indoor Positioning System

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Wireless Communications and Mobile Computing 2021年第1期2021卷

作者： Li, Ji Wang, Yepeng Chen, Zhuo Ma, Linlin Yan, Suqing Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China University of British Columbia Vancouver Canada Zhengzhou Locaris Technology Co. Ltd. Zhengzhou450000 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

Indoor 3D positioning system requires precise information from all three dimensions in space, but measurements in the vertical direction are usually interfered by sensors properties, unexpected obstructions, and other factors. Thus, accuracy and robustness are not guaranteed. Aiming at this problem, we propose a novel sensor fusion algorithm to improve the height estimation for a UWB-barometer integrated positioning system by introducing a pseudo reference update mechanism and the extended Kalman filter (EKF). The proposed fusion approach effectively helps with sensing noise reduction and outlier restraint. The results from numerical experiment investigations demonstrate that the accuracy and robustness of the proposed method achieved better improvement in height determination. © 2021 Ji Li et al.

关键词： Indoor positioning systems

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Small Object Detection via a Dense Connection and Feature Enhancement Network

Small Object Detection via a Dense Connection and Feature En...

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International Conference on Computer and Communications (ICCC)

作者： Baohua Qiang Lirui Chen Shuiping Guo Lingzhi Liu Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China The 7th Research Institute of China Electrics Technology Group Corporation Guangzhou China

When detecting small objects in complex environment, the features of small objects will become blurred or even lost as the number of network layers increases. To address this problem, we constructed a Dense Connection and Feature Extraction Network, termed DCFEN. First, we designed a dense-connected multi-scale feature enhancement framework, which can effectively extract and fuse multi-scale features. Second, we constructed a dense-connected subnet using dense connections, which enhances the propagation of features and the utilization of shallow feature information, improving the detection performance of small objects. Finally, extensive experimental results demonstrated the detection precision and superiority of our method.

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A Chinese Sentiment Analysis Model Incorporating Knowledge Graph and Deep Learning

A Chinese Sentiment Analysis Model Incorporating Knowledge G...

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2023 7th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Shengping Zhao Jinlong Chen Xingguo Qin School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi China

With the rapid growth of social media and e-commerce, users are generating large amounts of textual review data on these platforms. Analyzing and mining these comment data is important for governments, enterprises, and consumers to make decisions. Therefore, sentiment analysis, especially for Chinese comments, has become an area of wide interest in industry and academia. Aiming at the characteristics of Chinese online reviews, this paper applies the techniques of knowledge graph and deep learning around sentence-level sentiment analysis and designs a sentiment analysis model that integrates different information such as sentiment knowledge graph and serialized annotation in a deep learning model to improve the accuracy and effectiveness of sentiment analysis of Chinese online reviews. This is important for improving products and services, understanding user needs, and making more informed business and policy decisions.

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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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Cross-patch feature interactive net with edge refinement for retinal vessel segmentation

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Computers in Biology and Medicine 2024年 174卷 108443-108443页

作者： Kang, Ning Wang, Maofa Pang, Cheng Lan, Rushi Li, Bingbing Guan, Junlin Wang, Huadeng School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network's ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://***/kita0420/CFI-Net. © 2024 Elsevier Ltd

关键词： Blood vessels

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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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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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