Research Article
Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network
Table 5
Comparison of the proposed method with some previous research.
| Literature | Features | Classifier | Accuracy [%] |
| [29] | Complex symmetric Gabor filter bank and principal component analysis (PCA) | PCA + Euclidean norm | 98.8 |
| [31] | Wavelet based feature extraction and morphological operations + Dempster-Shafer theory | MLP neural networks | 89.48 |
| [32] | Radial basis function (RBF) network and gray level arrangement in the neighborhood of each pixel + PCA | PCA + RBF network with Gaussian kernel | 83.4 |
| [33] | Small scale overcomplete basis set and Gabor filter | Sparse coding | 93 |
| Proposed Methodology | Features derived from DCT transform of -image and FFN | PCA + FFN | 97.35 |
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