Intelligent droplet tracking with correlation filters for digital microfluidics
作者： Bingyong Yan , Cong Kong , Hui-Feng Wang , Zhen Gu , Jia-Le Zhou
DOI: 10.1016/J.CCLET.2021.05.002
关键词:
摘要: ABSTRACT Tracking the movement of droplets in digital microfluidics is essential to improve its control stability and obtain dynamic information for applications such as point-of-care testing, environment monitoring chemical synthesis. Herein, an intelligent, accurate fast droplet tracking method based on machine vision developed microfluidics. To continuously recognize transparent real-time avoid interferes from background patterns or inhomogeneous illumination, we introduced correlation filter tracker, enabling online learning multi-features Fourier domain. Results show proposed could accurately locate droplets. We also demonstrated capacity estimation velocity faster 20 mm/s, application Griess reaction both colorimetric assay nitrite study kinetics.
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