scholarly journals Automatic, Illumination-Invariant and Real-Time Green-Screen Keying Using Deeply Guided Linear Models

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1454
Author(s):  
Hanxi Li ◽  
Wenyu Zhu ◽  
Haiqiang Jin ◽  
Yong Ma
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Minimization ◽  
Linear Models ◽  
Segmentation Method ◽  
Whole Process ◽  
Controlled Illumination ◽  
Fully Automatic ◽  
Green Screen

The conventional green screen keying method requires users’ interaction to guide the whole process and usually assumes a well-controlled illumination environment. In the era of “we-media”, millions of short videos are shared online every day, and most of them are produced by amateurs in relatively poor conditions. As a result, a fully automatic, real-time, and illumination-robust keying method would be very helpful and commercially promising in this era. In this paper, we propose a linear model guided by deep learning prediction to solve this problem. The simple, yet effective algorithm inherits the robustness of the deep-learning-based segmentation method, as well as the high matting quality of energy-minimization-based matting algorithms. Furthermore, thanks to the introduction of linear models, the proposed minimization problem is much less complex, and thus, real-time green screen keying is achieved. In the experiment, our algorithm achieved comparable keying performance to the manual keying software and deep-learning-based methods while beating other shallow matting algorithms in terms of accuracy. As for the matting speed and robustness, which are critical for a practical matting system, the proposed method significantly outperformed all the compared methods and showed superiority over all the off-the-self approaches.

Development and Application of Quality Control System for Highway Subgrade Construction

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Tong Wang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Detection Method ◽  
System Development ◽  
Effective Control ◽  
Construction Process ◽  
Development Mode ◽  
The Road ◽  
Whole Process

The compaction quality of the subgrade is directly related to the service life of the road. Effective control of the subgrade construction process is the key to ensuring the compaction quality of the subgrade. Therefore, real-time, comprehensive, rapid and accurate prediction of construction compaction quality through informatization detection method is an important guarantee for speeding up construction progress and ensuring subgrade compaction quality. Based on the function of the system, this paper puts forward the principle of system development and the development mode used in system development, and displays the development system in real-time to achieve the whole process control of subgrade construction quality.

scholarly journals Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

2021 ◽  
Vol 38 (6) ◽  
pp. 1875-1885
Author(s):  
Ruchi Jayaswal ◽  
Manish Dixit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Preventive Measure ◽  
Face Mask ◽  
Histogram Equalization ◽  
Test Accuracy ◽  
The Face ◽  
Novel Coronavirus ◽  
Face Detector

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

scholarly journals Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

2020 ◽  
Author(s):  
Ramya Balakrishnan ◽  
Maria Valdes Hernandez ◽  
Andrew Farrall
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
White Matter ◽  
Clinical Research ◽  
White Matter Hyperintensities ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Vascular Origin ◽  
Automatic Methods ◽  
Fully Automatic

Background: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin. Method: We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2. Results: The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their method in public repositories. Conclusions: We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

scholarly journals SmartBark: An Automated Dog Barking Detection and Monitoring System using AI and Deep Learning

2021 ◽  
Author(s):  
Xiangjian Liu ◽  
Yishan Zou ◽  
Yu Sun
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Deep Learning ◽  
Everyday Life ◽  
Real Time ◽  
Monitoring System ◽  
Audio Clip ◽  
The Everyday ◽  
The Status

Dogs have the tendency to bark at loud noises that they perceive as an intruder or a threat, and the hostile barking can often last up to hours depending on the duration of such noise. These barking sessions are unnecessary and negatively impact the quality of life of the others in your community, causing annoyance to your neighbors [1]. Having the rights to file noise complaints to the Home Owners Association, potentially resulting in fines or even the removal of the pet [2]. In this paper, we will discuss the development of an algorithm that takes in audio inputs through a microphone, then processes the audio and identifies that the audio clip is dog barks through machine learning, and ultimately sends the notification to the user. By implementing our application to the everyday life of dog owners, it allows them to accurately determine the status of their dog in real-time with minimal false reports.

scholarly journals Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

2020 ◽  
Author(s):  
Ramya Balakrishnan ◽  
Maria Valdes Hernandez ◽  
Andrew Farrall
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
White Matter ◽  
Clinical Research ◽  
White Matter Hyperintensities ◽  
Magnetic Resonance Images ◽  
Segmentation Method ◽  
Vascular Origin ◽  
Automatic Methods ◽  
Fully Automatic

Background: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin. Method: We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2. Results: The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.93, being the highest value obtained from a deep learning segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their method in public repositories. Conclusions: Although deep learning methods reported highly accurate results, we found no evidence that favours them over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material to Objective Evaluation of Bowel Preparation Quality in Colonoscopy (Preprint)

2021 ◽  
Author(s):  
Yen-Po Wang ◽  
Ying-Chun Jheng ◽  
Kuang-Yi Sung ◽  
Hung-En Lin ◽  
I-Fang Hsin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Automatic Segmentation ◽  
Objective Evaluation ◽  
Manual Segmentation ◽  
Segmentation Method ◽  
Bowel Cleansing ◽  
Fully Automatic ◽  
Colon Preparation ◽  
Overlap Area

BACKGROUND Adequate bowel cleansing is important for a complete examination of the colon mucosa during colonoscopy. Current bowel cleansing evaluation scales are subjective with a wide variation in consistency among physicians and low reported rate. Artificial intelligence (AI) has been increasingly used in endoscopy. OBJECTIVE We aim to use machine learning to develop a fully automatic segmentation method to mark the fecal residue-coated mucosa for objective evaluation of the adequacy of colon preparation. METHODS Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation and verification datasets. The fecal residue was manually segmented by skilled technicians. Deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. TheA total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. RESULTS A total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. CONCLUSIONS We used machine learning to establish a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for objective evaluation of colon preparation.

scholarly journals Improving Imaging Quality of Real-time Fourier Single-pixel Imaging via Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4190 ◽  
Author(s):  
Saad Rizvi ◽  
Jie Cao ◽  
Kaiyu Zhang ◽  
Qun Hao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Quality ◽  
Imaging Quality ◽  
Convolutional Autoencoder ◽  
Image Set ◽  
Real Time Applications ◽  
The Cost ◽  
Single Pixel

Fourier single pixel imaging (FSPI) is well known for reconstructing high quality images but only at the cost of long imaging time. For real-time applications, FSPI relies on under-sampled reconstructions, failing to provide high quality images. In order to improve imaging quality of real-time FSPI, a fast image reconstruction framework based on deep learning (DL) is proposed. More specifically, a deep convolutional autoencoder network with symmetric skip connection architecture for real time 96 × 96 imaging at very low sampling rates (5–8%) is employed. The network is trained on a large image set and is able to reconstruct diverse images unseen during training. The promising experimental results show that the proposed FSPI coupled with DL (termed DL-FSPI) outperforms conventional FSPI in terms of image quality at very low sampling rates.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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