scholarly journals Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5236
Author(s):  
Bosheng Qin ◽  
Dongxiao Li
Keyword(s):  
Super Resolution ◽  
Classification Problem ◽  
Automatic Identification ◽  
Infectious Disease Control ◽  
The Public ◽  
Global Pandemic ◽  
Potential Applications ◽  
Public Dataset ◽  
Image Super Resolution ◽  
Facial Images

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.

scholarly journals Identifying Facemask-wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19

2020 ◽  
Author(s):  
BOSHENG QIN ◽  
DONGXIAO LI
Keyword(s):  
Super Resolution ◽  
Face Mask ◽  
Classification Problem ◽  
Automatic Identification ◽  
Classification Methods ◽  
Facial Image ◽  
Infectious Disease Control ◽  
Global Pandemic ◽  
Public Dataset ◽  
Image Super Resolution

Abstract Rapid worldwide spread of Coronavirus Disease 2019 (COVID 19) has resulted in a global pandemic. Correct facemask wearing is valuable in infectious disease control, but the effectiveness of facemasks has been diminished mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask wearing conditions. In this study, we developed a new facemask wearing condition identification method in combination with image super resolution with classification network (SRCNet) SRCNet), which quantified a three categories classification problem based on unconstrained 2D facial image images. The proposed algorithm contained four main steps: image pre processing, face detection and crop, image super resolution, and face mask wearing conditions identification. Our method was trained and evaluated on public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask wearing, 134 images of incorrect facemask wearing, and 3030 images of correct facemask wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end to end image classification methods using deep learning without image super resolution by over 1.5 in kappa. Our findings indicate that the proposed SRCNet could achieve high accuracy identification in facemask wearing conditions , which have potential application in epidemic prevention involving COVID 19.

scholarly journals Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

2018 ◽  
Author(s):  
Zheng Wang ◽  
Mang Ye ◽  
Fan Yang ◽  
Xiang Bai ◽  
Shin'ichi Satoh
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Feature Representation ◽  
Image Feature ◽  
Low Resolution ◽  
Open World ◽  
In Series ◽  
High Resolution Images ◽  
Public Dataset ◽  
Image Super Resolution

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

scholarly journals Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5940
Author(s):  
Natheer Khasawneh ◽  
Mohammad Fraiwan ◽  
Luay Fraiwan ◽  
Basheer Khassawneh ◽  
Ali Ibnian
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Local Data ◽  
X Ray ◽  
The Public ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Public Dataset

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

scholarly journals Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Weisen Pan ◽  
Jian Li ◽  
Lisa Gao ◽  
Liexiang Yue ◽  
Yan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Problem ◽  
Graph Classification ◽  
Method Performance ◽  
The Public ◽  
Semantic Graph ◽  
Public Dataset ◽  
Public Datasets ◽  
Better Than ◽  
Email Classification

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

OnPoint: A package for online experiments in motor control and motor learning

2020 ◽  
Author(s):  
Jonathan Sanching Tsay ◽  
Alan S. Lee ◽  
Guy Avraham ◽  
Darius E. Parvin ◽  
Jeremy Ho ◽  
...  
Keyword(s):  
Motor Control ◽  
Motor Learning ◽  
Open Source Software ◽  
Research Progress ◽  
Global Pandemic ◽  
Large N ◽  
Open Source Software Package ◽  
Potential Applications ◽  
Temporal And Spatial ◽  
The One

Motor learning experiments are typically run in-person, exploiting finely calibrated setups (digitizing tablets, robotic manipulandum, full VR displays) that provide high temporal and spatial resolution. However, these experiments come at a cost, not limited to the one-time expense of purchasing equipment but also the substantial time devoted to recruiting participants and administering the experiment. Moreover, exceptional circumstances that limit in-person testing, such as a global pandemic, may halt research progress. These limitations of in-person motor learning research have motivated the design of OnPoint, an open-source software package for motor control and motor learning researchers. As with all online studies, OnPoint offers an opportunity to conduct large-N motor learning studies, with potential applications to do faster pilot testing, replicate previous findings, and conduct longitudinal studies (GitHub repository: https://github.com/alan-s-lee/OnPoint).

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