Radiographic Weld Defect Identification using Statistical Texture and Bayes Classifier
Automatic radiographic weld defect identification involves pattern recognition techniques i.e feature extraction and classification. Various methods have been applied using geometrical and morphological measurement as feature descriptor and classification based on cost function optimization. In this work, a radiographic weld defect identification system has been developed based on statistical texture descriptor and statistical based classifier. Several steps were used in this work i.e. image pre-processing to reduce the image noise and to enhance the image contrast, second step was feature extraction using histogram statistical texture (HST) that extracted sixes features from the images, and the last step was weld defect classification using Bayes classifier. The experiment testing was done in order to compare the classification accuracy of Bayes classifier with other classification method that has been used in the previous work. The experimental result have proven that the classification accuracy using HST and Bayes classifier reach 90.6% and it is outperform than other methods.
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