Abstract
Surface defect detection of metallic surfaces is a major challenge in any manufacturing industry. In this paper, an automated system to classify alloy steel surface based on Nonsubsampled Contourlet Transform (NSCT) is presented. Firstly the images are decomposed into different scales and directional subbands using Nonsubsampled Contourlet Transform (NSCT). The nonsubsampled contourlet transform is built upon nonsubsampled pyramids and nonsubsampled directional filter banks and provides a shift invariant directional multiresolution image representation. The image is decomposed at various scales and directions and the energy features are extracted. The energy features of defect and non defective surface are extracted and the best set that distinguishes the surface is used for classification.
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