Eurasip Journal on Image and Video Processing最新文献_第7页

Reduced reference image and video quality assessments: review of methods 减少参考图像和视频质量评估：方法综述

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2022-01-12 DOI: 10.1186/s13640-021-00578-y

Shahi Dost, Faryal Saud, Maham Shabbir, Muhammad Gufran Khan, M. Shahid, B. Lovstrom

引用次数: 7

A study on implementation of real-time intelligent video surveillance system based on embedded module 基于嵌入式模块的实时智能视频监控系统的实现研究

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-11-21 DOI: 10.1186/s13640-021-00576-0

Kim, Jin Su, Kim, Min-Gu, Pan, Sung Bum

{"title":"A study on implementation of real-time intelligent video surveillance system based on embedded module","authors":"Kim, Jin Su, Kim, Min-Gu, Pan, Sung Bum","doi":"10.1186/s13640-021-00576-0","DOIUrl":"https://doi.org/10.1186/s13640-021-00576-0","url":null,"abstract":"Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"122 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2021-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Perceptual hashing method for video content authentication with maximized robustness 鲁棒性最大化的视频内容认证感知哈希方法

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-11-21 DOI: 10.1186/s13640-021-00577-z

Qiang Ma, Ling Xing

引用次数: 0

HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification HR-MPF:用于肺结节分割和分类的多尺度渐进融合的高分辨率表示网络

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-11-13 DOI: 10.1186/s13640-021-00574-2

Ling Zhu, Hongqing Zhu, Suyi Yang, Pengyu Wang, Yang Yu

引用次数: 1

Fatigue driving detection based on electrooculography: a review 基于眼电成像的疲劳驾驶检测研究进展

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-11-02 DOI: 10.1186/s13640-021-00575-1

Yuan-Qing Tian, Jingyu Cao

引用次数: 8

An image-guided network for depth edge enhancement 一种用于深度边缘增强的图像引导网络

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-10-18 DOI: 10.21203/rs.3.rs-958953/v1

Kuan-Ting Lee, Enyu Liu, J. Yang, Li Hong

引用次数: 1

Palpation localization of radial artery based on 3-dimensional convolutional neural networks 基于三维卷积神经网络的桡动脉触觉定位

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-10-18 DOI: 10.21203/rs.3.rs-965158/v1

Qiliang Chen, Yulin Huang, Xing Zhu, Hong Lu, Zhongzhi Ji, Jiacheng Yang, Jingjing Luo

{"title":"Palpation localization of radial artery based on 3-dimensional convolutional neural networks","authors":"Qiliang Chen, Yulin Huang, Xing Zhu, Hong Lu, Zhongzhi Ji, Jiacheng Yang, Jingjing Luo","doi":"10.21203/rs.3.rs-965158/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-965158/v1","url":null,"abstract":"Palpation localization is essential for detecting physiological parameters of the radial artery for pulse diagnosis of Traditional Chinese Medicine (TCM). Detecting signal or applying pressure at the wrong location can seriously affect the measurement of pulse waves and result in misdiagnosis. In this paper, we propose an effective and high accuracy regression model using 3-dimensional convolution neural networks (CNN) processing near-infrared picture sequences to locate radial artery upon radius at the wrist. Comparing with early studies using 2-dimensional models, 3Dcnn introduces temporal features with the third dimension to leverage pulsation rhythms, and had achieved superior performance accuracy as 0.87 within 50 pixels at testing resolution of 1024 × 544. Model visualization shows that the additional dimension of the temporal convolution highlights dynamic changes within image sequences. This study presents the great potential of our constructed model to be applied in real wrist palpation location scenarios to bring the key convenience for pulse diagnosis.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"2022 1","pages":"1-13"},"PeriodicalIF":2.4,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44158328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recognition of printed small texture modules based on dictionary learning 基于字典学习的印刷小纹理模块识别

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-09-29 DOI: 10.1186/s13640-021-00573-3

Yu, Lifang, Cao, Gang, Tian, Huawei, Cao, Peng, Zhang, Zhenzhen, Shi, Yun Q.

{"title":"Recognition of printed small texture modules based on dictionary learning","authors":"Yu, Lifang, Cao, Gang, Tian, Huawei, Cao, Peng, Zhang, Zhenzhen, Shi, Yun Q.","doi":"10.1186/s13640-021-00573-3","DOIUrl":"https://doi.org/10.1186/s13640-021-00573-3","url":null,"abstract":"Quick Response (QR) codes are designed for information storage and high-speed reading applications. To store additional information, Two-Level QR (2LQR) codes replace black modules in standard QR codes with specific texture patterns. When the 2LQR code is printed, texture patterns are blurred and their sizes are smaller than(0.5{mathrm{cm}}^{2}). Recognizing small-sized blurred texture patterns is challenging. In original 2LQR literature, recognition of texture patterns is based on maximizing the correlation between print-and-scanned texture patterns and the original digital ones. When employing desktop printers with large pixel extensions and low-resolution capture devices, the recognition accuracy of texture patterns greatly reduces. To improve the recognition accuracy under this situation, our work presents a dictionary learning based scheme to recognize printed texture patterns. To our best knowledge, it is the first attempt to use dictionary learning to promote the recognition accuracy of printed texture patterns. In our scheme, dictionaries for all kinds of texture patterns are learned from print-and-scanned texture modules in the training stage. And these learned dictionaries are employed to represent each texture module in the testing stage (extracting process) to recognize their texture pattern. Experimental results show that our proposed algorithm significantly reduces the recognition error of small-sized printed texture patterns.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"471 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Performance enhancement method for multiple license plate recognition in challenging environments 具有挑战性环境下的多车牌识别性能增强方法

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-09-17 DOI: 10.1186/s13640-021-00572-4

Khurram Khan, A. Imran, H. A. U. Rehman, A. Fazil, Muhammad Zakwan, Z. Mahmood

引用次数: 6

Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning 基于机器学习的5g无人机视频流服务缓冲评估模型及调度策略

IF 2.4 4区计算机科学

Eurasip Journal on Image and Video Processing Pub Date : 2021-08-23 DOI: 10.1186/s13640-021-00570-6

Su, Yu, Wang, Shuijie, Cheng, Qianqian, Qiu, Yuhe

{"title":"Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning","authors":"Su, Yu, Wang, Shuijie, Cheng, Qianqian, Qiu, Yuhe","doi":"10.1186/s13640-021-00570-6","DOIUrl":"https://doi.org/10.1186/s13640-021-00570-6","url":null,"abstract":"With regard to video streaming services under wireless networks, how to improve the quality of experience (QoE) has always been a challenging task. Especially after the arrival of the 5G era, more attention has been paid to analyze the experience quality of video streaming in more complex network scenarios (such as 5G-powered drone video transmission). Insufficient buffer in the video stream transmission process will cause the playback to freeze [1]. In order to cope with this defect, this paper proposes a buffer starvation evaluation model based on deep learning and a video stream scheduling model based on reinforcement learning. This approach uses the method of machine learning to extract the correlation between the buffer starvation probability distribution and the traffic load, thereby obtaining the explicit evaluation results of buffer starvation events and a series of resource allocation strategies that optimize long-term QoE. In order to deal with the noise problem caused by the random environment, the model introduces an internal reward mechanism in the scheduling process, so that the agent can fully explore the environment. Experiments have proved that our framework can effectively evaluate and improve the video service quality of 5G-powered UAV.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":"262 ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2