NDLI: Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

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Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

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Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

Content Provider	IET Digital Library
Author	Jayabal, Rajamohan Karuppiyan, Vijayarekha Sidharthan, Rakesh Kumar
Abstract	Dispersion nature of water droplets over the insulator surface is used for hydrophobicity classification. Stochastic nature of water dispersions makes naive Bayesian classifier a preferable choice, which has been investigated in this work. About 12 features describing the characteristics of water droplets are extracted from the binary image using binary large objects analysis. Ambient light intensity is a significant factor that affects the binary image quality. As these insulators are installed in the outside environment, variations in ambient light intensity are inevitable. An adaptive threshold technique is proposed to compensate for ambient light variations. Six classes of various ambient light intensities have been considered in this study, and the proposed adaptive threshold technique can produce quality binary image consistently. Features extracted from the binary image are ordered according to their principal components (PCs) using PC analysis. Improvement in classification accuracy with the accumulation of ordered features is analysed. Results illustrate the use of the first eight features provides a reliable classification accuracy of 97.6% for test image samples. In comparison to the other existing classifiers, the proposed classifier illustrates optimal performance in terms of classification accuracy and computational time.
Starting Page	324
Ending Page	332
Page Count	9
Volume Number	4
e-ISSN	23977264
Issue Number	Issue 4, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/hve/4/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/hve.2019.0052
Journal	High Voltage
Publisher	The Institution of Engineering and Technology CEPRI
Publisher Date	2019-06-19
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Adaptive Threshold Technique Ambient Light Compensation Ambient Light Intensity Ambient Light Variations Bayes Method Binary Image Features Binary Image Quality Binary Large Objects Analysis Computer Vision And Image Processing Technique Dispersion Nature Feature Extraction Hydrophobicity Classification Image Classification Insulator Surface Lighting Naive Bayesian Classifier Optical, Image And Video Signal Processing Ordered Feature Overhead Polymeric Insulator Pattern Classification Principal Component Analysis Quality Binary Image Reliable Classification Accuracy Statistics Stochastic Nature Test Image Sample Water Dispersions Water Droplets
Content Type	Text
Resource Type	Article

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