Data on Machine Vision Reported by Researchers at University of Massachusetts (Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing)
By a News Reporter-Staff News Editor at Science Letter -- New research on Machine Vision is the subject of a report. According to news reporting originating from Lowell, Massachusetts, by NewsRx correspondents, research stated, "This study addresses classification methodology for the automatic inspection of a range of defects on the surface of glass substrates in thin film transistor liquid crystal display glass substrate manufacturing. The proposed methodology consisted of four stages: (1) feature extraction by calculating the wavelet co-occurrence signature from the substrate images, (2) handling of imbalanced dataset using the Synthetic Minority Over-sampling TEchnique (SMOTE), (3) reduction of the feature's dimension by principal component analysis, and (4) finally choosing the best classifier between three different methods: Classification And Regression Tree (CART), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM)."
Our news editors obtained a quote from the research from the University of Massachusetts, "In training the SVM and MLP classifiers, the simulated annealing algorithm was used to obtain the optimal tuning parameters for the classifiers. From the industrial case study, the proposed feature extraction algorithm could remove the defect-irrelevant image features and SMOTE increased the accuracy of all three methods."
According to the news editors, the research concluded: "Furthermore, the optimized SVM and MLP models were more accurate than the CART model whereas a higher accuracy of 89.5% was observed for the proposed SVM model."
For more information on this research see: Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing. Journal of Process Control, 2014;24(6):1015-1023. Journal of Process Control can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Journal of Process Control - www.elsevier.com/wps/product/cws_home/30445)
The news editors report that additional information may be obtained by contacting A. Yousefian-Jazi, University of Massachusetts, Dept. of Chem Engn, Lowell, MA 01854, United States. Additional authors for this research include J.H. Ryu, S. Yoon and J.J. Liu (see also Machine Vision).
Keywords for this news article include: Lowell, Massachusetts, United States, Machine Vision, Machine Learning, Emerging Technologies, North and Central America
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