Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824328

Hong Zhou, Guoqiang Chen

引用次数: 0

Discerning Art Works through Active Machine Learning 通过主动机器学习识别艺术作品

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824180

Zihao Yu

引用次数: 0

Single-pixel imaging encryption based on 2D coupled Logistic mapping 基于二维耦合Logistic映射的单像素图像加密

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824618

Qian Wang, Yaoran Huo

引用次数: 0

Application research of plant leaf pests and diseases base on unsupervised learning 基于无监督学习的植物叶片病虫害应用研究

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824321

Mingjing Pei, Min Kong, MaoSheng Fu, Xiancun Zhou, Zusong Li, Jieru Xu

引用次数: 1

Overlapped Pedestrian Detection Based on YOLOv5 in Crowded Scenes 基于YOLOv5的拥挤场景重叠行人检测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825055

Wei-wu Guo, Nanbo Shen, Tingjuan Zhang

引用次数: 0

Malware Classification based on a Light-weight Architecture of CNN: MalShuffleNet 基于CNN轻量级架构的恶意软件分类:MalShuffleNet

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824719

Lin Qiu, Shuo Wang, Jian Wang, Yifei Wang, Wei Huang

引用次数: 1

Prediction of Iron Ore Spheroidity Based on Image Texture Features and PCA-SVR 基于图像纹理特征和PCA-SVR的铁矿石球度预测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825366

Daifei Liu, Shengyang Wang

{"title":"Prediction of Iron Ore Spheroidity Based on Image Texture Features and PCA-SVR","authors":"Daifei Liu, Shengyang Wang","doi":"10.1109/cvidliccea56201.2022.9825366","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825366","url":null,"abstract":"Spheroidity is an important parameter to describe the granulation characteristics of iron ore. Traditionally, physical and chemical analysis methods are used to obtain the spheroidization of iron ore. However, these processes are time-consuming and labor-intensive, and it is difficult to control the accuracy of the results. In this study, image processing and neural networks are used to construct a support vector regression (SVR) iron ore sphericity prediction model from the perspective of information fusion. Three kinds of image texture feature extraction methods are used: Tamura texture feature, gray level co-occurrence matrix (GLCM), and gray level difference statistics (GLDS). Principal component analysis are used to dimensionality reduction of image texture feature parameters. Under the same operating conditions, the results using the SVR model with and without PCA are compared, and the prediction accuracy of these models for iron ore spheroidity are 96.7% and 79.8%, respectively. The results show that the model based on image texture features and PCA-SVR has excellent characteristics, such as fast operating time and high accuracy, for the prediction of iron ore spheroidity, has practical significance in guiding the sintering process of iron ore and can provide further efficient and accurate research on iron ore spheroidity in the future.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"115 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80868993","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

Multi angle location and identification method of suspension insulators based on R2CNN algorithm 基于R2CNN算法的悬架绝缘子多角度定位与识别方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824037

Chao Hou, Yuchen Xing, Ziru Ma, Hai-Fen Liu, Shaotong Pei, Rui Yang, Zhilei Li

引用次数: 1

K-Pointer-Network for Express Delivery Routes Planning 速递路线规划的k -指针网络

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9823987

Xu Zheng

引用次数: 0

Multi-modal Brain Network Fusion Based on Random Walk-Grassmann Model 基于随机游走-格拉斯曼模型的多模态脑网络融合

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824548

Jiyun Li, Gege Wen, Chen Qian

{"title":"Multi-modal Brain Network Fusion Based on Random Walk-Grassmann Model","authors":"Jiyun Li, Gege Wen, Chen Qian","doi":"10.1109/cvidliccea56201.2022.9824548","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824548","url":null,"abstract":"Brain network plays an important role in the diagnosis of many brain diseases. At present, some related studies are based on the structural or functional connection group of human brain, while others consider the related properties of structural and functional brain networks at the same time. Aiming at the problems of how to dynamically collect richer node interaction information and how to learn more effectively from small samples in the research of brain network fusion, we propose a Random Walk-Grassmann (RW-GM) model to effectively fuse them. Firstly, we obtain the structural connection matrix and the temporal characteristic matrix of the brain from the multi-modal data of each subject. Then, we use random walk algorithm and Grassmann pooling method to integrate the two matrices, in order to integrate the structural connection and the temporal characteristics of the brain, so as to obtain more abundant brain connection information. In order to better carry out small sample learning, we use recursive feature elimination method for feature selection, and put the selected features into support vector machine to get the final classification result. We have carried out four binary classification experiments on ADNI data set, and the classification accuracy is better than that of traditional brain network classification methods.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"27 1","pages":"129-134"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82041520","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

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