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- W2366379256 abstract "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." @default.
- W2366379256 created "2016-06-24" @default.
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- W2366379256 date "2011-01-01" @default.
- W2366379256 modified "2023-09-22" @default.
- W2366379256 title "Feature Dimensions Reduction and Its Optimization for Steel Strip Surface Defect Based on Genetic Algorithm" @default.
- W2366379256 hasPublicationYear "2011" @default.
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