{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:35:07Z","timestamp":1771515307208,"version":"3.50.1"},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p> In this paper, an automatic defect classification algorithm for thin film transistor liquid crystal display (TFT-LCD) manufacturing is proposed. Each sample of defect data contains three images: the original image, the defect shape image and the circuit zone image. A set of features including shape, histogram and color is extracted. Some common classifiers were tested in the experiments and Linear-SVM (Linear Surport Vector Machine) was chosen in practical manufacturing. A novel LBP-E feature considering intensity equality proposed in this paper is compared to other original rotation invariant LBP (Local Binary Pattern) features. The experimental results show that our method can generate a better result with a relatively low dimension number. <\/jats:p>","DOI":"10.1142\/s0219467813500113","type":"journal-article","created":{"date-parts":[[2013,8,7]],"date-time":"2013-08-07T05:26:45Z","timestamp":1375853205000},"page":"1350011","source":"Crossref","is-referenced-by-count":6,"title":["AUTOMATIC DEFECT CLASSIFICATION OF TFT-LCD PANELS WITH SHAPE, HISTOGRAM AND COLOR FEATURES"],"prefix":"10.1142","volume":"13","author":[{"given":"WEI","family":"HUANG","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HONGTAO","family":"LU","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2013,8,26]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1080\/00207540410001716480"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(99)00009-8"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1007\/BF01211662"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1007\/s001380050133"},{"key":"rf9","volume-title":"Digital Image Processing","author":"Gonzalez R. C.","year":"1992"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017623"},{"key":"rf11","volume-title":"Introduction to Statistical Pattern Recognition","author":"Fukunaga K.","year":"1990"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-02230-2_7"}],"container-title":["International Journal of Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219467813500113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T17:42:51Z","timestamp":1565199771000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219467813500113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7]]},"references-count":10,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2013,8,26]]},"published-print":{"date-parts":[[2013,7]]}},"alternative-id":["10.1142\/S0219467813500113"],"URL":"https:\/\/doi.org\/10.1142\/s0219467813500113","relation":{},"ISSN":["0219-4678","1793-6756"],"issn-type":[{"value":"0219-4678","type":"print"},{"value":"1793-6756","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,7]]}}}