Abstract
This paper has presented an efficient solder joint inspection technique through the use of wavelet transform and Support Vector Machines. The proposed scheme consists of two stages: a feature extraction stage for extracting features with wavelet transform, and a classification stage for classifying solder joints with a support vector machines. Experimental results show that the proposed method produces a high classification rate in the nonlinearly separable problem of classifying solder joints.
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Soo Yun, T. (2001). Classification of Specular Object Based on Statistical Learning Theory. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_67
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DOI: https://doi.org/10.1007/3-540-45723-2_67
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