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Image identification of glass defects based on Non-Negative Matrix Factorization and Sparse Representation Classification

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3 Author(s)
Yang Bao ; Key Laboratory Of Advanced Process Control For Light Industry (Ministry Of Education), Jiangnan University, Wuxi 214122, China ; Zhu Qibing ; Huang Min

Identifying glass defects through machine visual has a vital importance for the efficient production of high quality glass. In this paper, a method based on the combination of Non-negative Matrix Factorization (NMF) and Sparse Representation Classification (SRC) was proposed for the identification of glass defects. According to the properties of glass defect image, NMF algorithm is used to decompose a defect image into one base image and another weighted coefficient matrix, so the defect image is characterized by the coefficient matrix. Then, SRC algorithm is used to classify glass defects. Simulation results show that, with NMF and SRC, glass defects images could be effectively identified.

Published in:

2012 24th Chinese Control and Decision Conference (CCDC)

Date of Conference:

23-25 May 2012