Search scope:
排序: Display mode:
Video summarization with a graph convolutional attention network Research Articles
Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6, Pages 902-913 doi: 10.1631/FITEE.2000429
Keywords: 时序学习;自注意力机制;图卷积网络;上下文融合;视频摘要
Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks Research Article
Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12, Pages 1848-1861 doi: 10.1631/FITEE.2200035
Keywords: Power systems Vulnerability Cascading failures Multi-graph convolutional networks Weighted line graph
Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit Research Articles
Dewen Seng, Fanshun Lv, Ziyi Liang, Xiaoying Shi, Qiming Fang,sengdw@hdu.edu.cn,172050041@hdu.edu.cn,liangziyi2020@163.com,shixiaoying@hdu.edu.cn,fangqiming@hdu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9, Pages 1179-1193 doi: 10.1631/FITEE.2000243
Keywords: 交通流量预测;多图卷积网络;门控循环单元;不规则区域
Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images Research Article
Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4, Pages 630-643 doi: 10.1631/FITEE.2000611
Keywords: Marine target detection Navigation radar Plane position indicator (PPI) images Convolutional neural network (CNN) Faster R-CNN (region convolutional neural network) method
Amultimodal dense convolution network for blind image quality assessment Research Article
Nandhini CHOCKALINGAM, Brindha MURUGAN
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11, Pages 1601-1615 doi: 10.1631/FITEE.2200534
Keywords: No-reference image quality assessment (NR-IQA) Blind image quality assessment Multimodal dense convolution network (MDSC-Net) Deep learning Visual quality Perceptual quality
Multi-focus image fusion based on fully convolutional networks Research Articles
Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900336
Keywords: 多焦距图像融合;全卷积网络;跳层;性能评估
A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG Regular Papers
Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3, Pages 405-413 doi: 10.1631/FITEE.1700413
Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.
Keywords: Convolutional neural networks (CNNs) Electrocardiogram (ECG) synthesis E-health
De-scattering and edge-enhancement algorithms for underwater image restoration Research Papers
Pan-wang PAN, Fei YUAN, En CHENG
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 862-871 doi: 10.1631/FITEE.1700744
Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.
Keywords: Image de-scattering Edge enhancement Convolutional neural network Non-subsampled contourlet transform
Deep 3D reconstruction: methods, data, and challenges Review Article
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000068
Keywords: 深度学习模型;三维重建;循环神经网络;深度自编码器;生成对抗网络;卷积神经网络
Recent advances in efficient computation of deep convolutional neural networks Review
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 64-77 doi: 10.1631/FITEE.1700789
Keywords: Deep neural networks Acceleration Compression Hardware accelerator
Aggregated context network for crowd counting
Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11, Pages 1535-1670 doi: 10.1631/FITEE.1900481
Keywords: 人群计数;卷积神经网络;密度估计;语义分割;多任务学习
Shot classification and replay detection for sports video summarization Research Article
Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5, Pages 790-800 doi: 10.1631/FITEE.2000414
Keywords: Extreme learning machine Lightweight convolutional neural network Local octa-patterns Shot classification Replay detection Video summarization
Two-level hierarchical feature learning for image classification Article
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9, Pages 897-906 doi: 10.1631/FITEE.1500346
Keywords: Transfer learning Feature learning Deep convolutional neural network Hierarchical classification Spectral clustering
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model Article
Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan
Engineering 2023, Volume 21, Issue 2, Pages 162-174 doi: 10.1016/j.eng.2021.11.021
Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e., ground rainfall stations or radar and satellite techniques) are available to measure and/or predict rainfall intensity, it is difficult to obtain accurate rainfall data with a desirable spatiotemporal resolution using these methods. This paper proposes an image-based deep learning model to estimate urban rainfall intensity with high spatial and temporal resolution. More specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phones or transportation cameras) and their corresponding measured rainfall intensity values. The trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based on the sensors' rainfall images. Synthetic rainfall data and real rainfall images are respectively utilized to explore the irCNN's accuracy in theoretically and practically simulating rainfall intensity. The results show that the irCNN model provides rainfall estimates with a mean absolute percentage error ranging between 13.5% and 21.9%, which exceeds the performance of other state-of-the-art modeling techniques in the literature. More importantly, the main feature of the proposed irCNN is its low cost in efficiently acquiring high spatiotemporal urban rainfall data. The irCNN model provides a promising alternative for estimating urban rainfall intensity, which can greatly facilitate the development of urban flood risk management in a real-time manner.
Keywords: Urban flooding Rainfall images Deep learning model Convolutional neural networks (CNNs) Rainfall intensity
A novel convolutional neural network method for crowd counting Research Articles
Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8, Pages 1119-1266 doi: 10.1631/FITEE.1900282
Keywords: Crowd counting Density estimation Segmentation prior map Uniform function
Title Author Date Type Operation
Video summarization with a graph convolutional attention network
Ping Li, Chao Tang, Xianghua Xu,patriclouis.lee@gmail.com
Journal Article
Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks
Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN
Journal Article
Forecasting traffic flows in irregular regions with multi-graph convolutional network and gated recurrent unit
Dewen Seng, Fanshun Lv, Ziyi Liang, Xiaoying Shi, Qiming Fang,sengdw@hdu.edu.cn,172050041@hdu.edu.cn,liangziyi2020@163.com,shixiaoying@hdu.edu.cn,fangqiming@hdu.edu.cn
Journal Article
Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images
Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com
Journal Article
Amultimodal dense convolution network for blind image quality assessment
Nandhini CHOCKALINGAM, Brindha MURUGAN
Journal Article
Multi-focus image fusion based on fully convolutional networks
Rui Guo, Xuan-jing Shen, Xiao-yu Dong, Xiao-li Zhang,zhangxiaoli@jlu.edu.cn
Journal Article
A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG
Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU
Journal Article
De-scattering and edge-enhancement algorithms for underwater image restoration
Pan-wang PAN, Fei YUAN, En CHENG
Journal Article
Deep 3D reconstruction: methods, data, and challenges
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Journal Article
Recent advances in efficient computation of deep convolutional neural networks
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Journal Article
Aggregated context network for crowd counting
Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn
Journal Article
Shot classification and replay detection for sports video summarization
Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk
Journal Article
Two-level hierarchical feature learning for image classification
Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE
Journal Article
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model
Hang Yin, Feifei Zheng, Huan-Feng Duan, Dragan Savic, Zoran Kapelan
Journal Article