An Efficient Feature Fusion Technique for Surface Grading of Ceramic Tiles
نویسندگان
چکیده
Automated grading system plays an important role in many industries. In ceramic industries, the grading of ceramic tiles is a difficult task as it has huge variations of surface properties. In this paper, automated surface grading system based on Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrices (GLCM) is presented. Texture information’s in ceramic tiles are effectively represented by DWT at various decomposition levels. From the decomposition, DWT energy features are extracted. Also, the statistical features; correlation, contrast, energy, homogeneity are extracted from the GLCM of ceramic tiles. Feature fusion is employed for effective classification using Feed Forward Neural Network (FFNN) classifier. The evaluation of the system is carried on VxC TSG image database. Experimental results show that the proposed surface grading system produces 99.18% accuracy using 4 level DWT features along with GLCM features th
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