Feature Dimensions Reduction and Its Optimization for Steel Strip Surface Defect Based on Genetic Algorithm

作者：Yu Hou · 年份：2011 · 被引用次数：3 · 研究领域：Advanced Measurement and Detection Methods、Industrial Vision Systems and Defect Detection、Image and Video Stabilization

The 32-dimensional feature vectors of intensity,texture and geometry characteristics for six kinds of steel strip surface typical defects images were extracted.The 32-dimensional feature vectors of steel strip surface defect images were reduced and optimized based on genetic algorithm,and 20-dimensional feature vectors were selected to classify types of the defects images.The recognition and classification experiments were carried out to contrast the 32-dimensional feature vectors with 20-dimensional feature ones with BP neural network for six kinds of steel strip surface typical defects images.Furthermore,the results using genetic algorithm was contrasted with principal component analysis.It is shows that the algorithms of features extraction and genetic algorithm are effective.