The intensity-based features extraction was evaluated and presented. This paper demonstrates visualization results of transformer oil gray as well as enhanced images. The image histogram quantifies the significant occurrence of pixel values. In this paper, GLCM texture features estimated in order to notice and discriminate the textures of the transformer oil images under ages. GLCM texture features such as homogeneity, contrast, correlation, energy, and entropy are evaluated for all the test images. The results indicate that the features evaluated were significantly identifying texture features. This paper concludes that the performance of GLCM texture features quantify the essential features of test images with and without noise eliminating filters. The extension of this study can be utilized to monitor the condition of transformer oil of in service transformers.
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