Vision最新文献_第10页

Target detection on lightweight device based on Compressed YOLOv5s6 基于压缩YOLOv5s6的轻量级设备目标检测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825360

Jingxian Cui, Weimin Zhou, Weijun Liu

引用次数: 0

Comparison between Machine Learning Models and Neural Networks on Music Genre Classification 机器学习模型与神经网络在音乐体裁分类中的比较

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825050

Zizhi Ma

{"title":"Comparison between Machine Learning Models and Neural Networks on Music Genre Classification","authors":"Zizhi Ma","doi":"10.1109/cvidliccea56201.2022.9825050","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825050","url":null,"abstract":"In terms of music genre classification, neural networks and machine learning models have their respective advantages. This paper aims to compare the performance and feature extraction capability between neural networks and traditional machine learning algorithms on music genre classification. All the components of 9 main music features, each with seven statistical values, were extracted as essential features, and different dimension reduction methods were applied. This paper compares the performance of training the features by neural networks and machine learning models. Finally, this paper used the output of layers in the neural networks as features and applied traditional machine learning models for training to see if their performance could be optimized. The result showed that the performance was raised by about 20%, compared to the essential features, and raised by about 5%, compared to the reduced features. So, it can be concluded that the feature extraction capability of neural networks is better than traditional machine learning models. Also, using features filtered by neural networks and applying traditional machine learning models for training is a method providing both excellent performance and high efficiency.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"43 1","pages":"189-194"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77418758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on urban open space behavior extraction based on semantic segmentation technology 基于语义分割技术的城市开放空间行为提取研究

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824593

X. Liu, Chenqi Li, Yu Chen

引用次数: 0

A Grapevine Virus Disease Detection Method Based on Convolution Neural Network 基于卷积神经网络的葡萄病毒病害检测方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825086

Yi Wang, Shuizhou Ke, Shaohong Wang, Zhibo Zheng

{"title":"A Grapevine Virus Disease Detection Method Based on Convolution Neural Network","authors":"Yi Wang, Shuizhou Ke, Shaohong Wang, Zhibo Zheng","doi":"10.1109/cvidliccea56201.2022.9825086","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825086","url":null,"abstract":"Black rot, black measles and isariopsis leaf spot are three kinds of very fatal grapevine virus disease. In the cultivation of grape, these diseases will harm the growth of grapes and have a great impact on the yield. Thus, timely diagnosis and treatment measures in the early stage of disease will greatly reduce the mortality of grape, which is particularly important in the cultivation of grape. The traditional method of manual screening requires staff with professional knowledge of diseases and detection experience, which requires high labor cost and a lot of time in large-scale detection. We consider adding a convolution neural network based deep learning detection method in large-scale screening to quickly detect easily diagnosed cases so as to focus on the hard-to-discern cases and reduce work pressure. In this paper, we propose a detection scheme using advanced deep learning framework to identify these three diseases with similar symptoms, locate their positions in image visualization and outline them accurately. Numerical results reveal that the detection scheme has great performance, and the high-performance configuration is obtained through several experiments.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"41 1","pages":"36-40"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77686823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Influence of different image preprocessing methods on bone age prediction 不同图像预处理方法对骨龄预测的影响

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825218

Yang Pan

{"title":"Influence of different image preprocessing methods on bone age prediction","authors":"Yang Pan","doi":"10.1109/cvidliccea56201.2022.9825218","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825218","url":null,"abstract":"In medical image recognition represented by bone age prediction, image samples need to be preprocessed to improve the quality of image samples and improve the learning efficiency of deep learning. This paper aims to compare the effects of different image preprocessing methods on the performance of the neural network. In this paper, the method of control experiment is used. Without pretreatment, the structure and framework of the neural network are controlled to remain unchanged, to make the conclusion more objective. This paper mainly discusses three pretreatment methods. 1 Conventional image filtering; 2. Use u-net network specially used for biomedical image segmentation to segment hand bones in X-ray; 3. The control group did not undergo image preprocessing. At the same time, this paper proposes to mark the gender of the owner of hand bone X-ray film in the form of a white background mark on the original image and control the gender weight by adjusting the size of the mark. U-net network preprocessing does not significantly improve the accuracy of the neural network, but this method makes the effect of deep neural network and shallow neural network almost the same, so it can be used as an effective method to prevent overfitting of neural networks. The main innovation of this paper is to explore the effectiveness of preprocessing algorithms in preventing the overfitting of medical image models by comparing the bone age prediction under various preprocessing methods.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"8 1","pages":"632-636"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75926074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lightweight Encryption Algorithm for RFID System 一种RFID系统的轻量级加密算法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824845

Wenhua Cao, Shuqin Geng, Xiaohong Peng, Jingyao Nie, Xuefeng Li, Pengkun Li

引用次数: 0

DMTP: A Distributed Matchmaking Trading Platform DMTP:分布式配对交易平台

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825350

Bowen Duan, Aiqing Du, Peiyu Yang, Jiale Wang, Wenjiei Mou, Haiyu Ju

引用次数: 1

Background Suppression Method of Star Image Based on Improved CBDNet 基于改进CBDNet的星图背景抑制方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825243

Yabo Li, Z. Niu, Quan Sun, Huaitie Xiao

引用次数: 0

Ancient Character Image Classification Model Training 古文字图像分类模型训练

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824485

Yi Lin

引用次数: 0

Research of the 51% attack based on blockchain 基于区块链的51%攻击研究

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824528

Yuechen Hao

{"title":"Research of the 51% attack based on blockchain","authors":"Yuechen Hao","doi":"10.1109/cvidliccea56201.2022.9824528","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824528","url":null,"abstract":"With the explosion of Nakamoto’s paper, blockchain technology has developed rapidly, but at the same time, security problems are emerging one after another. As a potential security hazard in the payment field, 51% attack brings huge risks to the normal operation of the blockchain system. Miners with great computing power have the ability to monopolize the generation of blocks and modify the generated blocks. Therefore, it is necessary to do research of this kind of attacks. This article cites both Nakamoto model and Rosenfeld model to illustrate relationship between computing power and attack success rate. Through a series of mining experiments, this paper preliminarily introduces the operation principles of blockchain based on Ethereum, including proof of stake, smart contracts etc. Models show that for rational attackers who pursue interests, they lack the motivation to launch 51% attacks. For attackers who only destroy the bitcoin system, they need to master huge financial resources to launch 51% of attacks, and even need financial support at the national level, which is very difficult. It can be said that 51% attacks against bitcoin are only theoretically possible, but users still need to pay enough attention. As an emerging technology, blockchain technology is currently in the research and exploration stage. While it is applied in the financial field, it is also expanding to other fields. In the future, blockchain technology will not only be used to solve the trust and security problems in the centralized service architecture, but also appear in more decentralized service scenarios. So, the research on blockchain security is particularly important.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"09 1","pages":"278-283"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86502960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1