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Automatic static contact angle algorithm for blurry drop images and its application in hydrophobicity measurement for insulating materials
Author(s) -
Xu Zhiniu
Publication year - 2015
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2014.0072
Subject(s) - hough transform , contact angle , drop (telecommunication) , computer vision , transformation (genetics) , artificial intelligence , viewing angle , optics , materials science , computer science , image (mathematics) , physics , telecommunications , composite material , biochemistry , chemistry , liquid crystal display , gene
To improve the accuracy of the static contact angle evaluation for the blurry drop images, a large number of water drop images are numerically generated or acquired by the contact angle instrument/digital camera. Based on these images, the applicability of the circle Hough transformation and circle fitting algorithms is systematically investigated and compared. The results reveal that the least‐squares circle fitting algorithm is particularly suitable for the clear drop images, such as the images acquired by the contact angle instrument. The circle Hough transformation is particularly suitable for the blurry drop images, such as the images acquired by the digital camera. The influence of the contact angle on the contact angle error by the circle Hough transformation is investigated. The critical contact angle values corresponding to various water drop volumes are given, which can be used to improve the accuracy of the static contact angle measurement for the blurry drop images. A modified Hough transformation based on the critical contact angle is proposed. The modified Hough transformation is verified by the static contact angle measurements of different insulating materials. The study proposes a comparatively effective algorithm for automatic and accurate static contact angle measurement of the blurry drop images.

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