Bubble Pattern Recognition from Particle Image Velocimetry (PIV) Images using a Deep-Learning-Based Image Processing Technique

2021 ◽  
Author(s):  
Rafael Franklin Lazaro de Cerqueira ◽  
Marco Antônio Cerutti ◽  
Emilio Paladino
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Image Velocimetry ◽  
Deep Learning ◽  
Particle Image ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Velocimetry

scholarly journals A Review of Planar PIV Systems and Image Processing Tools for Lab-On-Chip Microfluidics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3090 ◽  
Author(s):  
Fahrettin Ergin ◽  
Bo Watz ◽  
Nicolai Gade-Nielsen
Keyword(s):  
Image Processing ◽  
Particle Image Velocimetry ◽  
Particle Image ◽  
Relevant Information ◽  
Droplet Formation ◽  
Field Investigation ◽  
Cell Counting ◽  
Lab On Chip ◽  
Micro Particle Image Velocimetry ◽  
Image Velocimetry

Image-based sensor systems are quite popular in micro-scale flow investigations due to their flexibility and scalability. The aim of this manuscript is to provide an overview of current technical possibilities for Particle Image Velocimetry (PIV) systems and related image processing tools used in microfluidics applications. In general, the PIV systems and related image processing tools can be used in a myriad of applications, including (but not limited to): Mixing of chemicals, droplet formation, drug delivery, cell counting, cell sorting, cell locomotion, object detection, and object tracking. The intention is to provide some application examples to demonstrate the use of image processing solutions to overcome certain challenges encountered in microfluidics. These solutions are often in the form of image pre- and post-processing techniques, and how to use these will be described briefly in order to extract the relevant information from the raw images. In particular, three main application areas are covered: Micro mixing, droplet formation, and flow around microscopic objects. For each application, a flow field investigation is performed using Micro-Particle Image Velocimetry (µPIV). Both two-component (2C) and three-component (3C) µPIV systems are used to generate the reported results, and a brief description of these systems are included. The results include detailed velocity, concentration and interface measurements for micromixers, phase-separated velocity measurements for the micro-droplet generator, and time-resolved (TR) position, velocity and flow fields around swimming objects. Recommendations on, which technique is more suitable in a given situation are also provided.

Particle Image Velocimetry Based on a Lightweight Deep Learning Model

2020 ◽  
Vol 40 (7) ◽  
pp. 0720001
Author(s):  
于长东 Yu Changdong ◽  
毕晓君 Bi Xiaojun ◽  
韩阳 Han Yang ◽  
李海云 Li Haiyun ◽  
郐云飞 Gui Yunfei
Keyword(s):  
Particle Image Velocimetry ◽  
Deep Learning ◽  
Particle Image ◽  
Learning Model ◽  
Image Velocimetry ◽  
Deep Learning Model

scholarly journals Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease

2017 ◽  
Vol 107 ◽  
pp. 85-104
Author(s):  
Raju Anitha ◽  
S. Jyothi ◽  
Venkata Naresh Mandhala ◽  
Debnath Bhattacharyya ◽  
Tai-hoon Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection ◽  
Processing Technique ◽  
Image Processing Technique

Time-resolved particle image velocimetry algorithm based on deep learning

2022 ◽  
pp. 1-1
Author(s):  
Chunyu Guo ◽  
Yiwei Fan ◽  
Changdong Yu ◽  
Yang Han ◽  
Xiaojun Bi
Keyword(s):  
Particle Image Velocimetry ◽  
Deep Learning ◽  
Particle Image ◽  
Time Resolved ◽  
Image Velocimetry

scholarly journals Health Detection of Wheat Crop Using Pattern Recognition and Image Processing

2020 ◽  
Vol 15 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Balwant Ram ◽  
Mamoon Rashid ◽  
Kamlesh Lakhwani ◽  
Shibi S. Kumar
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Human Resources ◽  
Agricultural Sector ◽  
Value Added ◽  
Processing Technique ◽  
Vital Role ◽  
Image Processing Technique ◽  
Wheat Crop ◽  
Recognition Method

Agriculture plays a vital role in India's economy. 44% of the employment in India is engaged in agriculture and allied activities and it also contributes 17% of the gross value added. As most of the country's people are in the agricultural sector and out of them only a few are literate about how to protect their cultivation ultimately gives rise to severe problems like a low economy in the sector and starvation for the nation. The job of this research is to help the farmers to save crops from disease. The authors came with the thought of combining a pattern recognition method and an image processing technique. The system allows a farmer to follow a particular pattern of growing crops so that threats will be analyzed earlier. Combining this with the power of Internet of Things, the authors can automate the process without the need for human resources. This research can ultimately make the agriculture process faster and farmers can cultivate more in a less amount of time.

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