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Surface Defect Inspection of Cold Rolled Strips with Features Based on Adaptive Wavelet Packets
Chang Su LEE Chong-Ho CHOI Young CHOI Se Ho CHOI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1997/05/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing,Computer Graphics and Pattern Recognition
surface defect inspection, wavelet packet, quadtree, subband decomposition, neural network,
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The defects in the cold rolled strips have textural characteristics, which are nonuniform due to its irregularities and deformities in geometrical appearance. In order to handle the textural characteristics of images with defects, this paper proposes a surface inspection method based on textural feature extraction using the wavelet transform. The wavelet transform is employed to extract local features from textural images with defects both in the frequency and in the spatial domain. To extract features effectively, an adaptive wavelet packet scheme is developed, in which the optimum number of features are produced automatically through subband coding gain. The energies for all subbands of the optimal quadtree of the adaptive wavelet packet algorithm and four entropy features in the level one LL subband, which correspond to the local features in the spatial domain, are extracted. A neural network is used to classify the defects of these features. Experiments with real image data show good training and generalization performances of the proposed method.