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Pattern Recognition
Real-time vision-based system for textile fabric inspection
Real-Time Imaging
Texture segmentation using wavelet transform
Pattern Recognition Letters
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International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
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Engineering Applications of Artificial Intelligence
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
Surface roughness vision measurement in different ambient light conditions
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
Mathematical methods to quantify and characterise the primary elements of trophic systems
International Journal of Computer Applications in Technology
Research on adaptive classification algorithm based on non-segment and classified-centre-vector
International Journal of Intelligent Information and Database Systems
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International Journal of Computer Applications in Technology
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International Journal of Computer Applications in Technology
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The surface quality would directly influence the capability and quality of the final product, but there is little domestic research focused on surface defects inspection for copper strip based on automated visual inspection. According to the gradual change of intensity levels of copper strips surface defect, a defect detection algorithm is proposed using wavelet-based multivariate statistical analysis. First, the image is divided into several sub-images, namely statistical units, and then each unit is further decomposed into multiple wavelet processing units. Then each wavelet processing unit is decomposed by 1D db4 wavelet function. Then, multivariate statistics of Hotelling T² are applied to distinguish the existence of defects and classify the defects using support vector machine (SVM). During SVM design, the authors used cross-validation method to get the best parameters and then used the parameters to train and test the samples. Finally, the defect detection performance of the proposed approach is compared with the traditional method based on greyscale. Experimental results demonstrate that the proposed method has better performance on identification, especially its application in the ripple defects can achieve a 96.7% probability of detecting the existence of micro defects, which was poor in common algorithms.