کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7131840 | 1461688 | 2018 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Image registration for a UV-Visible dual-band imaging system
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی برق و الکترونیک
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
The detection of corona discharge is an effective way for early fault diagnosis of power equipment. UV-Visible dual-band imaging can detect and locate corona discharge spot at all-weather condition. In this study, we introduce an image registration protocol for this dual-band imaging system. The protocol consists of UV image denoising and affine transformation model establishment. We report the algorithm details of UV image preprocessing, affine transformation model establishment and relevant experiments for verification of their feasibility. The denoising algorithm was based on a correlation operation between raw UV images, a continuous mask and the transformation model was established by using corner feature and a statistical method. Finally, an image fusion test was carried out to verify the accuracy of affine transformation model. It has proved the average position displacement error between corona discharge and equipment fault at different distances in a 2.5m-20â¯m range are 1.34â¯mm and 1.92â¯mm in the horizontal and vertical directions, respectively, which are precise enough for most industrial applications. The resultant protocol is not only expected to improve the efficiency and accuracy of such imaging system for locating corona discharge spot, but also supposed to provide a more generalized reference for the calibration of various dual-band imaging systems in practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics and Lasers in Engineering - Volume 105, June 2018, Pages 209-218
Journal: Optics and Lasers in Engineering - Volume 105, June 2018, Pages 209-218
نویسندگان
Tao Chen, Shuang Yuan, Jianping Li, Sheng Xing, Honglong Zhang, Yuming Dong, Liangpei Chen, Peng Liu, Guohua Jiao,