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5th EAI international conference on advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI international conference on advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945534

The proceedings contain 94 papers. The special focus in this conference is on advanced Hybrid Information processing. The topics include: A Dynamic Monitoring Method for Marathon Athletes Based on Wireless Sensor Network;research on Key Technologies of Analysis of User Emotion Fluctuation Characteristics in Wireless Network Based on Social Information processing;Variable Bandwidth Receiving Method of Civil Aircraft Radar signal Based on FPGA Technology;recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring;a Real-Time Detection Algorithm for Abnormal Users in Multi Relationship Social Networks Based on Deep Neural Network;research on Personalized Recommendation Algorithm Based on Mobile Social Network Data;building Construction Information Real-Time Sharing Method Based on 3D Scanning Technology and Social Network Analysis;a Mining Algorithm for Relevance of Business Administration Based on Complex Social Information Network;classification Method of Regional Differentiation Characteristics of Enterprise Management;design of Fractal image Coding Compression and Transmission Model Based on Wavelet Transform;research on Price Stickiness of Consumer Goods Based on Real-Time Social Information Flow;an Algorithm of Employment Resource Allocation for College Students Based on Social Network Mining;an Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation;A Novel Method of Combined Window Function Construction Filter Applied to F-OFDM System;Passive Electromagnetic Field Positioning Method Based on BP Neural Network in Underwater 3-D Space;optimization of Cross-border E-commerce Marketing Strategy During the Pandemic;deep Residual Network with Transfer Learning for High Spatial Resolution Remote Sensing Scenes Classification;research on E-commerce Logistics Transportation Route Planning Method Based on Recurrent Neural Network.

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Multiple Attention-Guided Neural Architecture Search for Hyperspectral image Classification 4

Multiple Attention-Guided Neural Architecture Search for Hyp...

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4th international conference on Video, signal and image processing, VSIP 2022

作者： Song, Yingluo Wang, Aili Dai, Shiyu Wu, Haibin Iwahori, Yuji Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin150080 China Department of Computer Science Chubu University Aichi487-8501 Japan

ISBN: (纸本)9781450397810

Hyperspectral images(HSIs) have rich spatial and spectral information, which provide a strong basis for distinguishing different landcover objects. The HSI classification methods based on deep learning have achieved good performance. Recently, several Neural Architecture Search (NAS) algorithms have been proposed for HSI to further improve the accuracy to a new level. In this paper, the neural structure search method based onmultiple attention mechanism search space is proposed for hyperspectral images. In the search process, the network selects attention mechanism operation at an appropriate layer, which can help the network focus on useful features and discard useless features. In our experiments, we have verified the superiority of this method on two hyperspectral datasets. © 2022 ACM.

关键词： image classification

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Multiview Human Action Recognition System Based on OpenPose and KNN Classifier 11th

Multiview Human Action Recognition System Based on OpenPose ...

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11th international conference on Robotics, Vision, signal processing and Power Applications, RoViSP 2021

作者： Malik, Najeeb Ur Rehman Abu Bakar, Syed Abdul Rahman Sheikh, Usman Ullah Computer Vision Video and Image Processing Lab ECE Department Universiti Teknologi Malaysia Johor Malaysia

ISBN: (纸本)9789811681288

Human action recognition is one of the trending research topics in the field of computer vision. Human-computer interaction and video monitoring are broad applications that aid in the understanding of human action in a video. The problem with action recognition algorithms such as 3D CNN, Two-stream network, and CNN-LSTM is that they have highly complex models including a lot of parameters resulting in difficulty while training them. Such models require high configuration machines for real-time human action recognition. Therefore, present research proposes the use of 2D skeleton features along with a KNN classifier based HAR system to overcome the aforementioned problems of complexity and response time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Automatic Pavement Crack Detection Based on YOLOv5-AH 12

Automatic Pavement Crack Detection Based on YOLOv5-AH

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12th international conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Dong, Zhaohui Zhu, Guijie Fan, Zhun Liu, Jiacheng Li, Huanlin Cai, Yuwei Huang, Huaxing Shi, Ze Ning, Weibo Wang, Liu Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou515063 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Object detection on pavement cracks plays a significant role in the overall condition assessment of the pavement. However, the automatic crack detection remains a tough work due to the complex background and the scale change. In order to solve the related issues, a deep learning model named YOLOv5-AH is proposed on the basis of the YOLOv5. We integrate convolutional block attention module (CBAM) to extract attention area, which can emphasize the crack features and suppress the background information. Moreover, we optimize the network architecture by adding one more prediction head to detect different-scale cracks. To further improved our proposed YOLOv5-AH, a series of advanced strategies are integrated into the model training including data augmentation. A pavement crack dataset in different enviroments is built by a mobile robot. The precision and recall of our crack detection method reach 0.924 and 0.988 respectively. The prediction speed of the model can achieve real-time effect (72FPS). © 2022 IEEE.

关键词： Crack detection

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ATTENTION BASED NETWORK FOR NO-REFERENCE UGC VIDEO QUALITY ASSESSMENT

ATTENTION BASED NETWORK FOR NO-REFERENCE UGC VIDEO QUALITY A...

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IEEE international conference on image processing (ICIP)

作者： Yi, Fuwang Chen, Mianyi Sun, Wei Min, Xiongkuo Tian, Yuan Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Tencent Social Commun Lab Shenzhen Peoples R China

ISBN: (纸本)9781665441155

The quality assessment of user-generated content (UGC) videos is a challenging problem due to the absence of reference videos and their complex distortions. Traditional no-reference video quality assessment (NR-VQA) algorithms mainly target specific synthetic distortions. Less attention has been paid to authentic distortions in UGC videos, which are not distributed evenly in both the spatial and temporal domains. In this paper, we propose an end-to-end neural network model for UGC video quality assessment based on the attention mechanism. The key step in our approach is to embed the attention modules in the feature extraction network, which effectively extracts local distortion information. In addition, to exploit the temporal perception mechanism of the human visual system (HVS), the gated recurrent unit (GRU) and temporal pooling layer are integrated into the proposed model. We validate the proposed model on three public in-the-wild VQA databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm. Experimental results demonstrate that the proposed method outperforms state-of-the-art NR-VQA models. The implementation of our method is released at https://***/qingshangithub/AB-VQA.

关键词： No-reference video quality assessment user-generated content videos attention mechanism

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2021 international conference in Advances in Power, signal, and Information Technology, APSIT 2021

2021 International Conference in Advances in Power, Signal, ...

引用

2021 international conference in Advances in Power, signal, and Information Technology, APSIT 2021

ISBN: (纸本)9781665425063

The proceedings contain 109 papers. The topics discussed include: a hybrid approach of deep learning algorithms for identification of COVID-19 disease using chest x-ray images;image encryption using chaotic techniques: a survey study;performance evaluation of photovoltaic based distributed generation against neural network based feedback control;solution to economic dispatch problem incorporating micro grid with integration of wind energy using emperor penguin optimization algorithm;an enhanced dynamic image watermarking framework based on phase congruency and saliency;review of blockchain-based energy trading models;breast cancer data analysis using machine learning approaches;and advanced reactive power control technique for wind power application.

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Application and Research of Dark Channel Defogging Algorithm in Video Logging image Enhancement 6

Application and Research of Dark Channel Defogging Algorithm...

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Dong, Haifeng Ma, Ruini Xi'an Shiyou University School of Computer Science Xi'an710065 China

ISBN: (纸本)9780738143705

The video logging technology is applied to detect the general condition of the well. Due to the influence of water fog, the contrast of the video logging image is decreased and the color contrast is distorted. The application and research of the dark channel dehazing algorithm in the video logging image enhancement system is proposed. In this paper, the dark channel defogging algorithm and Retinex defogging algorithm are compared and analyzed respectively, the realization process and application of the two algorithms are described briefly, and the advantages and disadvantages of the two algorithms applied in video logging image enhancement are compared. © 2021 IEEE.

关键词： image enhancement

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Impacts of Losses Functions on the Quality of the Ultrasound image by Using Machine Learning algorithms

Impacts of Losses Functions on the Quality of the Ultrasound...

引用

2021 IEEE international conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Dangoury, Soufiane Abouzahir, Saad Alali, Abdelhakim Sadik, Mohammed Hassan II University of Casablanca Morocco

ISBN: (纸本)9781665403436

During last decade, Artificial Intelligence (AI) has been able to reshape our life daily. Different areas were positively impacted by AI such as Healthcare, Logistic, etc. Medical imaging is one of the fields of healthcare in which AI was introduced to solve and overcome different problems. Challenges including image processing, signal processing, and data acquisition. In this paper, we deeply demonstrate the loss function as one of the main parameters that influence the quality of the ultrasound (US) image. Therefore, we introduce the main components of ultrasound systems form end-to-end perspective such as the data acquisition, the signal processing, and the image interpretation. Then, we present the losses functions as a critical performance metrics for the model validation. Metrics such as the Mean Absolute Error (MAE), Cross-Entropy loss function (CE), Dice Similarity Coefficient (DSC), and the Structural Similarity (SSIM). After that we present the adopted CNN model to generate ultrasound image. The excessive simulation results demonstrate that the selection of the loss function provides significant improvement in terms of image quality (e.g., contrast, CNR and SNR). Choosing simple loss functions such as mean square error helps to faster the convergence of the convolution neural network during the training process. However, for image quality enhancement, we propose the combination of different loss functions such structural similarity (SSIM) with Dice Similarity Coefficient (DSC). © 2021 IEEE.

关键词： Data acquisition

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AVHYAS: A Free and Open Source QGIS Plugin for advanced Hyperspectral image Analysis

AVHYAS: A Free and Open Source QGIS Plugin for Advanced Hype...

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2021 international conference on Emerging Techniques in Computational Intelligence, ICETCI 2021

作者： Lyngdoh, Rosly Boy Sahadevan, Anand S Ahmad, Touseef Rathore, Pradyuman Singh Mishra, Manoj Gupta, Praveen Kumar Misra, Arundhati Advanced Microwave and Hyperspectral Techniques Development Group Space Applications Centre Isro Hyperspectral Techniques Development Division Gujarat Ahmedabad India

ISBN: (纸本)9781665415590

advanced Hyperspectral Data Analysis Software (AVHYAS) plugin is a Python-3 based Quantum-GIS (QGIS) plugin designed to process and analyse hyperspectral (Hx) images. Starting with version 1.0, AVHYAS serves as a free and open-source platform for sharing and distributing Hx data analysis methods among research scholars, scientists and potential end-users. It is developed to guarantee full usage of present and future Hx airborne or spaceborne sensors and provides access to advanced algorithms for Hx data processing. The software is freely available and offers a range of basic and advanced tools such as atmospheric correction (for airborne AVIRIS-NG image), standard processing tools as well as powerful machine learning and Deep Learning interfaces for Hx data analysis. This paper gives an overview of the AVHYAS plugin, explains typical workflows and use cases for making it a constantly used platform for hyperspectral remote sensing applications. © 2021 IEEE.

关键词： Python

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image Fusion-Based Watermarking in IWT-SVD Domain 1

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3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Singh, Om Prakash Singh, Amit Kumar National Institute of Technology Patna India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

To protect the copyright of the digital media, a robust watermarking algorithm based on image fusion, integer wavelet transform (IWT)-singular value decomposition (SVD) and hybrid of optimization scheme is developed. The algorithm considers IWT to decompose the carrier image into non-overlapping sub-bands, and then SVD is performed on lower frequency sub-bands to compute the singular matrices. Further, the algorithm utilizes the stationary wavelet transform (SWT) to decompose the multi-focus images into several sub-bands, and obtain the fused image as mark image. To provide the solution of false positive problem (FPP), we conceal principal component (PC) of the fused mark the carrier image with optimal embedding factor. Prior to this, fusion of Firefly and PSO optimization scheme is used to determine the embedding strength, which provide a good relationship between robustness and quality. Experimental demonstration shows that compared with the traditional algorithms, robustness of the proposed algorithm is significantly improved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Singular value decomposition

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