Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825333

Yong Sun, W. Yang, Guangzhao Lu, Jinqing Zhao, Xiaoyue Wang, Ji-rong Xue

引用次数: 0

Non-frontal face recognition method with a side-face-correction generative adversarial networks 基于侧脸校正生成对抗网络的非正面人脸识别方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9825237

Haixin Lin, Hongzhi Ma, Weibin Gong, Chao Wang

{"title":"Non-frontal face recognition method with a side-face-correction generative adversarial networks","authors":"Haixin Lin, Hongzhi Ma, Weibin Gong, Chao Wang","doi":"10.1109/cvidliccea56201.2022.9825237","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825237","url":null,"abstract":"Frontal face image recognition is the main target of traditional face recognition.The deflection of the human face often causes the dislocation of the facial features,which leads to the reduction of the recognition accuracy of the non-frontal face.To solve the above problems,a non-frontal face recognition model based on generative adversarial network is proposed.In this model,the angle information is encoded separately by using a two-channel generator and auto-coding network,and the non-frontal face image in natural environment is corrected to obtain the frontal face image.Through the multi-discriminator mechanism of facial attention,we set discriminators in the eye, eyebrow, nose, mouth and the whole area of the face image so as to retain the details of the face to the maximum extent while ensuring the clarity of image.Then the corrected face features are extracted by Facenet and MTCNN to obtain the non-frontal face recognition results.The model is validated on multi-PIE dataset and CFP dataset.The results show that the accuracy of non-frontal face recognition is improved by 1% in CFP dataset compared with VGG-FACE, TP- CNN and HPN.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"625 1","pages":"563-567"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78971103","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: 3

Identification Method of Dress Pattern Drawing based on Machine Vision Algorithm 基于机器视觉算法的服装图案识别方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824224

Keun-Jong Lyu, Haizhang Yan

引用次数: 3

Book recommendation system based on an optimized collaborative filtering algorithm 基于优化协同过滤算法的图书推荐系统

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824088

Yujie Lu, Yidi Lu

引用次数: 1

Blockchain-based power battery traceability system for new energy vehicles 基于区块链的新能源汽车动力电池溯源系统

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824472

Yan Ma, Ruoyu Fang

引用次数: 1

Hyperspectral Image Denoising Based on Multi-Resolution Gated Network with Wavelet Transform 基于小波变换的多分辨率门控网络高光谱图像去噪

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824964

Kengpeng Li, Fenfa Zhong, Lei Sun

{"title":"Hyperspectral Image Denoising Based on Multi-Resolution Gated Network with Wavelet Transform","authors":"Kengpeng Li, Fenfa Zhong, Lei Sun","doi":"10.1109/cvidliccea56201.2022.9824964","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824964","url":null,"abstract":"Hyperspectral image denoising is an essential pre-processing task. In this paper, a multi-resolution gated network based on wavelet transform (WMRGNet) is proposed for removing mixed noise of hyperspectral images. Firstly, based on the fact that hyperspectral images have strong spectral correlation, a spatial-spectral information extraction module is designed to use the current noisy band and its adjacent bands as the input of WMRGNet. Secondly, aim to fully consider the spatial local and global information of hyperspectral images, a multi-resolution feature extraction module is proposed, applying the discrete wavelet transform to divide the resolution into four scales, and the residual blocks to extract information of different resolutions. In addition, a gated layer is introduced for cross-resolution information interaction to enhance the feature fusion. Finally, a high-resolution image reconstruction module with multiple residual blocks is employed to extract high-resolution features. In the simulated data set experiments, WMRGNet removes Gaussian, stripe and deadline noise and preserves the detailed information of the hyperspectral images.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"56 1","pages":"637-642"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88935014","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

Smart Noise Jamming Suppression Method Based on Target Position Estimation 基于目标位置估计的智能噪声干扰抑制方法

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824130

Lei Qiu, Yize Fan

引用次数: 0

Attention and Memory Training System Based on Neural Feedback 基于神经反馈的注意力和记忆训练系统

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824137

Xiujun Li, LiMin Tang, Zhilin Zhang, Jinglong Wu, Qi Li

{"title":"Attention and Memory Training System Based on Neural Feedback","authors":"Xiujun Li, LiMin Tang, Zhilin Zhang, Jinglong Wu, Qi Li","doi":"10.1109/cvidliccea56201.2022.9824137","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824137","url":null,"abstract":"With the rapid development of the society, people’s cognitive ability is gradually declining under the pressure of study and work, those declining will bring a great impact on people’s daily life, work and study. Therefore, it has become a popular research field on the intervention of early declining cognitive ability. It has been proved that a brain-computer interface (BCI), as a new tool of neural feedback training, can improve the traditional neural feedback method’s efficiency. However, due to the weakness of EEG signals, the accuracy of character’s recognition in BCI system is still low, and the spelling paradigm is often dominated by the characters output, which makes it difficult for the subjects’ attention to continuously focus on the whole experiment process. In order to improve the recognition accuracy and effectively reduce subjects’ fatigue as well as increase the concentration of the participant, this study proposed a spelling paradigm that can more effectively stimulate the Event Related Potential (ERP) of the subject. In the end, a cognitive ability training system was designed for daily training. Through the system simulation and preliminary experiments, the average target recognition accuracy increased by 18.75% after the experiment, demonstrating the efficiency and effectiveness of the present cognitive ability training system.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"6 1","pages":"794-798"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87541152","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

Sitting Posture Detection System Based on Keras Framework 基于Keras框架的坐姿检测系统

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824164

Peng Yang, Zhirong Peng, Wu-Long Wang

引用次数: 0

Detection of Cells and Microbes in Microscopic Field Based on Improved YOLOv5 基于改进YOLOv5的显微场细胞和微生物检测

Vision Pub Date : 2022-05-20 DOI: 10.1109/cvidliccea56201.2022.9824054

Xu Chu, Xiaoyang Liu

引用次数: 0

Vision最新文献