Abstract
In many practical problems wavelet theory offers methods to handle data in different scales. It is highly adaptable to represent data in a compact and sparse way without loss of information. We present an approach to find and classify defects on specular surfaces using pointwise extracted features in scale space. Our results confirm the presumption that the stationary wavelet transform is better suited to localize surface defects than the classical decimated transform. The classification is based on a support vector machine (SVM) and furthermore applicable to empirically evaluate given wavelets for specific classification tasks and can therefore be used as quality measure.
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Hahn, A., Ziebarth, M., Heizmann, M., Rieder, A. (2013). Defect Classification on Specular Surfaces Using Wavelets. In: Kuijper, A., Bredies, K., Pock, T., Bischof, H. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2013. Lecture Notes in Computer Science, vol 7893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38267-3_42
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DOI: https://doi.org/10.1007/978-3-642-38267-3_42
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