Abstract
Fractal analysis has been applied as a useful tool to quantitatively evaluate the streamer structure in insulating liquids. However, a fixed global greyscale threshold in the image binarization process can cause inevitable background noises and increase the uncertainty of the fractal analysis. This paper focuses on enhancing the fractal analysis by developing a dynamic greyscale threshold algorithm. The greyscale threshold for each pixel is dynamically estimated for a more accurate image binarization process. A unique background recognition method composed of two critical greyscale values is proposed to further reduce background noise in the binary streamer image. From the sensitivity study done in this paper, the square width in the algorithm was optimized at 80 pixels, while the difference between the two critical greyscale values for background recognition is set in the range of 25–40. The dynamic greyscale threshold algorithm is successfully applied to images of negative streamers obtained in five insulating liquids to enhance the fractal analyses.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
