A Modified Singular Value Decomposition Kernelized Neutrosophic Entropy Method for TFT-LCD Panel Defect Segmentation

Distinct defects will inevitability incur due to multiple layers of production are required to manufacture TFT-LCD panels. Localization and segmentation of defects are vital in monitoring the panels to improve the yields. But accurate segmentation of defect areas is challenging due to the complex/diverse defects, illumination artifacts, the similarity of the defective pixels with the neighboring pixels, and excessive overall colors in the image. Furthermore, defects have distinct shapes, types, sizes, and locations. This study proposed a singular value decomposition Kernelized Neutrosophic entropy (SVDKNE) method to resolve these challenges that can enhance the inhomogeneous defect images adaptively. Finally, the comparative analysis on a dataset with 309 images validates that the SVDKNE method outperforms other five methods in locating and segmenting defects with higher values of average Jaccard similarity of 0.93, structural similarity of 0.99, and peak signal-to-noise ratio of 38.78.