2023 7th International Conference on Machine Vision and Information Technology (CMVIT)最新文献

A Novel Cross Grouping CG MLP based on local mechanism 一种新的基于局部机制的交叉分组CG MLP

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00021

Hang Xu, Tao Wang, Wei Wen, Xingyu Liu

引用次数: 0

Improved Root Sparse Bayesian Learning for DOA Estimation in Non-uniform Noise 基于改进根稀疏贝叶斯学习的非均匀噪声DOA估计

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00017

Yifan Zhang, Hangfang Zhao

引用次数: 0

Review of the Application of Machine Vision in Aquaculture UAVs 机器视觉在水产养殖无人机中的应用综述

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00022

Yang Zhiling

引用次数: 0

Improved Convolutional 3D Networks for Micro-Movements Recognition 微运动识别的改进卷积三维网络

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00026

Rui Yuan, Lihua Zhang

{"title":"Improved Convolutional 3D Networks for Micro-Movements Recognition","authors":"Rui Yuan, Lihua Zhang","doi":"10.1109/cmvit57620.2023.00026","DOIUrl":"https://doi.org/10.1109/cmvit57620.2023.00026","url":null,"abstract":"It is of great significance for computers to recognize the actions in videos. The human body’s action recognition has been applied in many fields. The majority of action recognition methods have relatively low precision in recognizing micro-movements. In some specific scenarios, tasks such as intelligent home companionship for the elderly and early warning for dangerous driving behaviors, the micro-actions of the observed are extremely important in the recognition task. At the same time, due to the physiological characteristics of the elderly or the limitation of the environment, the amplitude of the actions is relatively small. This research suggests an action recognition method based on deep learning to better analyze micro-movements-oriented action recognition. Inspired by transformer, we split an image into fixed-size patches. The network structure of C3D is improved. The idea of image patch is introduced to reduce the receptive field of each region in the video frame. Finally, the experimental verification is performed on two action recognition datasets, UCF101 and NTU. The average accuracies on UCF101 and NTU respectively are 91.74% and 88.01%, which show that the proposed algorithm can effectively improve the recognition ability of micro-movements and obtain better results compared with other baselines.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277436","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 new method for producing temperature profiles based on ERA5 and RAOB 基于ERA5和RAOB的温度剖面生成新方法

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00016

Yale Qiao

{"title":"A new method for producing temperature profiles based on ERA5 and RAOB","authors":"Yale Qiao","doi":"10.1109/CMVIT57620.2023.00016","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00016","url":null,"abstract":"Temperature profiles are important meteorological parameters of the atmosphere that can determine atmospheric thermal processes. Detecting global spatial and temporal continuous atmospheric temperature profiles is crucial for weather protection work. Atmospheric datasets such as ERA5 (fifth generation ECMWF reanalysis) provide global and continuous temperature profile datasets with good resolution. RAOB (radiosonde) sounding data have high confidence and representativeness and are commonly used for data accuracy validation. In this paper, we use the RAOB sounding data of 2017 as the true value and revise the ERA5 reanalysis data based on machine learning methods to optimize the data. The algorithm not only improves the problem of RAOB distribution discontinuity but also improves the accuracy of ERA5 itself. In order to verify the results of the algorithm, the RAOB sounding data are compared with it, and it is found that the accuracy of the revised data is reduced by about 3K compared to the preprocessing RMSE, which is closer to the RAOB data. The algorithm proposed in this paper can provide important data support for subsequent meteorological studies.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279730","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

Fast Estimation of Direction of Arrival for Towed Array Based on Sparse Bayesian Learning 基于稀疏贝叶斯学习的拖曳阵列快速到达方向估计

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00014

Zican Zhang, Xiang Pan

引用次数: 0

The influence and remodeling of artificial intelligence technology to China’s news dissemination industry : ——Taking the application of Baidu Brain AI core technology engine as an example 人工智能技术对中国新闻传播行业的影响与重塑:——以百度大脑AI核心技术引擎的应用为例

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00037

Boxiong Song

引用次数: 0

Image Dehazing based on Multi-scale Feature Fusion under Attention Mechanism 注意机制下基于多尺度特征融合的图像去雾

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00024

Shaotian Wang, Guihui Chen

{"title":"Image Dehazing based on Multi-scale Feature Fusion under Attention Mechanism","authors":"Shaotian Wang, Guihui Chen","doi":"10.1109/CMVIT57620.2023.00024","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00024","url":null,"abstract":"To solve the problems of insufficient feature extraction and the loss of too much image information in existing methods, a dehazing network based on multi-scale feature fusion under attention mechanism is proposed. Firstly, the base convolutional layer in U-Net is built using improved fully connected residual blocks to reduce the amount of computation. Secondly, the self-convolution block based on the self-attention mechanism is added to extract more delicate feature information of the image. Finally, to increase feature reuse and reduce feature information loss, the feature maps of different levels are fused using various scale gated units. In order to improve the capacity of the restored image to be recognized subjectively, the mixed loss function of multi-scale structural similarity and minimal absolute error is introduced. Experiments are carried out with synthetic haze data sets. Compared with other neural networks, the multi-scale structural similarity and peak signal-to-noise of the dehazed image of the proposed network are increased by 4.31% and 18.33% on average, respectively. The experiment results demonstrate that the network can efficiently avoid color distortion, halo and strong edge effect around the object, and the image has high subjective recognition after haze removal.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"100 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114010558","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 Target Detection of Regional Monitoring with Complex Background using CNN and Background Modelling 基于CNN和背景建模的复杂背景下区域监测目标检测研究

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00028

Weichen Sun, ZhanHua Yang, Bo Zhao, Y. Wang, Zhonglin Yang, Yutong Jiang, Haiping Song

引用次数: 0

High-Automatical and High-Accurate Pupil Location Neural Network via FRST FPL 基于FRST FPL的高自动化高精度瞳孔定位神经网络

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00018

Hong‐lei Ma, Ran Shen, Jing Ye, Huajun Su, Hantian Xie, Han Jiang

{"title":"High-Automatical and High-Accurate Pupil Location Neural Network via FRST FPL","authors":"Hong‐lei Ma, Ran Shen, Jing Ye, Huajun Su, Hantian Xie, Han Jiang","doi":"10.1109/CMVIT57620.2023.00018","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00018","url":null,"abstract":"Pupil location refers to the location of the pupil or its center in an image. To solve the problem that the pupil location method is difficult to achieve high automation and high accuracy at the same time, this paper proposes a method combining image processing and statistical learning. In this paper, an improved algorithm of the fast radial symmetry transform (FRST) based on pupil location is proposed, namely FRSTFPL (fast radial symmetry transform for pupil location), which is used to coarsely localize the pupil in the image, followed by a shallow CNN to achieve precise localization. In addition, we construct a dataset based on the CASIA-IrisV4 iris image database and then conduct a variety of experiments. The results show that the location error of the proposed method in an image with a size of 640 × 480 pixels is 8.51 pixels, which exceeds the performance of the comparing methods. In our method, not only accurate radius and complex network are unnecessary, but also highly automated, low computational complexity, and relatively high localizing accuracy can be achieved together.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284090","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