Abstract
In this paper, a method is described for the classification of corrosion images into two distinct classes. Since segmentation is very difficult, an automatic feature selection and classification procedure is preferred. This is done by performing a wavelet decomposition of the images, and computing energy signatures from the decomposition. Compact signature vectors represent the images and effectively characterize their type. The recognition is performed with a Learning Vector Quantization network. The method is tested on a set of 398 images, 260 of which were for training. High recognition scores are obtained.
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© 1995 Springer-Verlag Berlin Heidelberg
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Livens, S. et al. (1995). Classification of corrosion images by wavelet signatures and LVQ networks. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_341
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DOI: https://doi.org/10.1007/3-540-60268-2_341
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