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2007 International Conference on Artificial Intelligence and Pattern recognition (AIPR-07) 403 Copper Clad Laminate Defects Classification Using Inverse Wavelets Transform and Support Vector Machine Te-Sheng Li Minghsin University of Science and Technology Department of Industrial Engineering and Management 1 Hsin-hsin Rd. Hsin-fong, Hsinchu County, Taiwan 30401, R.O.C. [email protected] Abstract In this paper, we present a multi-resolution approach for the inspection local defects embedded in homogeneous copper clad laminate (CCL) surfaces. The proposed method is based mainly on the wavelet transform and inverse wavelet transform on the subimages by properly selecting the adequate decomposition levels. The restored image will remove regular, repetitive texture patterns and enhance only local anomalies. Based on these local anomalies, feature extraction methods can then be used to discriminate between the defective regions and homogeneous regions in the restored image. Real samples with five classes of defects have been classified using this novel multi-classifier, namely, support vector machine. The experimental results show the efficacy of the proposed method. 1. Introduction Visual inspection plays a vital role of quality control in the manufacturing system. Manual inspection is subjective and highly dependent on the inspector’s expertise. In this study, we use machine vision and image processing techniques combining a novel classifier, support vector machine, to detect and classify copper clad laminate (CCL) surface defects. Most CCL surface defects are tiny involving obvious faulty items such as pine holes, stains, scratches, strips and other ill-defined defects. These unanticipated defects are small in size, refractive in light and cannot be described using explicit measures, making automatic defect detection difficult. Because CCL defects are similar to texture patterns, the inspection task in this study can be classified as texture detection and classification. Most defect detection methods for texture surfaces generally involve computing a set of textural features in a sliding window. The system searches for significant local deviations in the feature values. The most difficult task in this approach is feature extraction and feature selection which completely embody the texture information in the image. There is no arbitrary approach for selecting and judging the appropriate features to use. Numerous methods have been proposed to extract textural features either directly from the spatial domain or from the spectral domain. In the spatial domain, the more reliable and commonly used features are the second-order statistics derived from spatial grey-level co-occurrence matrices [1-2]. They have been applied to wood inspection [3], carpet wear assessment [4], and roughness measurements for machined surfaces [5]. Typically, the spectral-domain features are generally less sensitive to noise than spatial-domain feature due to the relatively uniform representation in the spectral domain. Recently,
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