Recognition and classification for steel strip surface defect images based on rough set theory
粗糙集理论的带钢表面缺陷图像的识别与分类

The 20dimensional feature vectors of intensity, texture and geometry characteristics for six kinds of steel strip surface typical defects images are extracted. The key technology of Rough Set theory is described. The decision table of the steel strip surface images recognition is created, the reduction for decision table is carried out, and the decision rules are obtained from the training sample images directly. The test samples of the steel surface defect images have been classified with application of decision rules, and then compare with the BP neural network algorithm. The recognition and classification of steel strip surface typical defects images based on rough set theory is effective.