Metal Defect Detection Using Random Threshold and Wiener Filter
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 5
Abstract
Quality of all substances like metals and other materials needs to be verified in advance for industrial use. Traditional system to defect and deficiency detection uses only grey level method to classify defects but due to its low productivity it is not worthy. With the help of image processing algorithm we have proposed a system to detect as well as classify defects in this paper. Morphological operations are used to detected defects from the pre-processed images. To characterize the irregular area processes like GLCM attributes, extraction of moment, geometric algorithm are used. Independent from the load level, an accurate method like back propagation networks for neural systems are used in classification which can satisfy both detection and classification problem.
Authors and Affiliations
Nuruddin Faizee, Anup Kumar, Deepak Khanwalkar, Vanmathi C
Keywords
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- EP ID EP20436
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