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

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

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E80-D No.5 pp.594-604

Publication Date: 1997/05/25

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Category: Image Processing,Computer Graphics and Pattern Recognition

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Chang Su LEE, Chong-Ho CHOI, Young CHOI, Se Ho CHOI, "Surface Defect Inspection of Cold Rolled Strips with Features Based on Adaptive Wavelet Packets" in IEICE TRANSACTIONS on Information, vol. E80-D, no. 5, pp. 594-604, May 1997, doi: .
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e80-d_5_594/_p

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@ARTICLE{e80-d_5_594,
author={Chang Su LEE, Chong-Ho CHOI, Young CHOI, Se Ho CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Surface Defect Inspection of Cold Rolled Strips with Features Based on Adaptive Wavelet Packets},
year={1997},
volume={E80-D},
number={5},
pages={594-604},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Surface Defect Inspection of Cold Rolled Strips with Features Based on Adaptive Wavelet Packets
T2 - IEICE TRANSACTIONS on Information
SP - 594
EP - 604
AU - Chang Su LEE
AU - Chong-Ho CHOI
AU - Young CHOI
AU - Se Ho CHOI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1997
AB - 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.
ER -