Title :
Image identification of glass defects based on Non-Negative Matrix Factorization and Sparse Representation Classification
Author :
Bao, Yang ; Qibing, Zhu ; Min, Huang
Author_Institution :
Key Lab. of Adv. Process Control For Light Ind. (Minist. Of Educ.), Jiangnan Univ., Wuxi, China
Abstract :
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.
Keywords :
computer vision; feature extraction; glass; image classification; matrix decomposition; sparse matrices; NMF; SRC; glass defect base image; glass defect image identification; glass quality; machine vision; nonnegative matrix factorization; sparse representation classification; weighted coefficient matrix; Classification algorithms; Electronic mail; Glass; Matrix decomposition; Principal component analysis; Sparse matrices; Support vector machines; Feature Extraction; Identification of Glass Defects; Non-negative Matrix Factorization; Sparse Representation Classification;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6243081
