Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

Znamenskaya, I. A.; Doroshchenko, I.A.; Tatarenkova, D. I.

doi:10.51130/graphicon-2020-2-3-15

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…To analyze unsteady gas flows, a neural network was built to classify the shadowgraph picture dataset and identify images with shock waves [132]. A separate CNN model was also developed to perform the regression job and describe the location of shock waves.…”

Section: Classification and Detection Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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Section: Classification and Detection Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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“…In the present study the two software tools were made for flow structures automatic detection and tracking. The first tool is our in-house code for shock wave detection based on the modified Canny edge detection and Hough transform algorithms [15,16]. Edge detection is used to represent possible shock wave boundaries and the Hough transform is used to find boundaries close to the straight line.…”

Section: Computer Vision and Machine Learningmentioning

confidence: 99%

“…Some of them are featured in our online gallery [17]. The detailed information about the software is given in [15,16,18]. Fig.…”

Section: Computer Vision and Machine Learningmentioning

confidence: 99%

Animations Analysis in Experimental Fluid Dynamics

Znamenskaya¹,

Sysoev²,

Doroshchenko³

2021

Self Cite

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“…In the present study we made two software for automatic flow structures detection and tracking. The first one is our in-house code for shock wave detection based on the modified Canny edge detection and Hough transform algorithms [15,16]. Edge detection is used to represent possible shock wave boundaries and the Hough transform is used to find boundaries close to the straight line.…”

Section: Computer Vision and Machine Learningmentioning

confidence: 99%

“…Some of them are featured in our online gallery [17]. The detailed information about the software is given in [15,16]. Figure 7 shows the example frames of the post-discharge thermal plume development and its automatic detection by the neural network.…”

Section: Computer Vision and Machine Learningmentioning

confidence: 99%

Experimental Digital Animation in Fluid Dynamics

Znamenskaya

Sysoev

Doroshchenko

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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Digital imaging became one of the main tools for studying unsteady flows. Modern high-speed cameras support video recording at high frame rates which makes it possible to study extended high-speed processes. We demonstrate here different animations: water temperature field evolution with a frame rate of 115 Hz; high-speed shadowgraph visualisation of different flows - water jet formation process (100 000 frames / s), shadowgraph animations of the shock waves created by the pulsed discharges (124 000 frames / s). Also, as an example of plasma flow visualization technique, we offer 9 sequential images of the shock wave - pulse gas discharge visualization obtained by the high-speed CCD camera with the 100 ns delay between frames. We developed in-house software based on the machine vision and learning techniques for automatic flow animations processing. The examples of the automatic oblique shock detection using Canny edge detection and Hough transform and thermal plume detection based on the pre-trained convolutional neural network are provided and discussed.

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Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

Cited by 9 publications

References 20 publications

Front-end deep learning web apps development and deployment: a review

Front-end deep learning web apps development and deployment: a review

Animations Analysis in Experimental Fluid Dynamics

Experimental Digital Animation in Fluid Dynamics

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