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The first module is designed to automatically identify the TCL in a TBC image using a histogram-based approach. The second module recognizes the microstructures in the TCL using a local thresholding-based method. This article extends the previous work by introducing convolutional neural networks (CNNs) to enhance the performance of the second module. The experimental results show that the CNN-based methods outperform local thresholding-based methods, and results of the proposed porosity measure are comparable to that of the domain experts.<\/p>","DOI":"10.4018\/ijmdem.2018100103","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T14:51:04Z","timestamp":1553698264000},"page":"40-58","source":"Crossref","is-referenced-by-count":4,"title":["A Fully Automated Porosity Measure for Thermal Barrier Coating Images"],"prefix":"10.4018","volume":"9","author":[{"given":"Wei-Bang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Engineering and Computer Science, Virginia State University, Petersburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin N","family":"Standfield","sequence":"additional","affiliation":[{"name":"Department of Engineering and Computer Science, Virginia State University, Petersburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[{"name":"Google Inc., Mountain View, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Economics, Virginia State University, Petersburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Technology, Virginia State University, Petersburg, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Zimmerman","sequence":"additional","affiliation":[{"name":"Commonwealth Center for Advanced Manufacturing, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"issue":"6","key":"IJMDEM.2018100103-0","first-page":"213","article-title":"Classification of copper alloys microstructure using image processing and neural network.","volume":"9","author":"O.Abouelatta","year":"2013","journal-title":"The Journal of American Science"},{"key":"IJMDEM.2018100103-1","first-page":"1251","article-title":"Dynamic thresholding of gray-level images.","author":"J.Bernsen","year":"1986","journal-title":"Proceedings of 8th Internation Conference on Pattern Recognition"},{"key":"IJMDEM.2018100103-2","first-page":"1759","article-title":"Text extraction using adaptive thresholding.","volume":"5","author":"S.Borole","year":"2014","journal-title":"International Journal of Computer Science and Information Technologies"},{"key":"IJMDEM.2018100103-3","first-page":"1","article-title":"The importance of what\u2019s not there: Porosity, Spraytime","volume":"19","author":"R. 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