{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T01:40:35Z","timestamp":1718329235370},"reference-count":17,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2016,1,28]],"date-time":"2016-01-28T00:00:00Z","timestamp":1453939200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Natural Science Foundation of Shanxi Province, China","award":["2012021011-5"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2017,2,10]]},"abstract":"Summary<\/jats:title>Online recognition of glass defects plays an important role in improving the quality of glass products. This paper presents a conjugate gradient neural network\u2010based method, which combines phase map and the defective image for recognition of glass defects. The boundary coordinates of the connected defect region are calculated and used to extract the defect region in the defective image correspondingly. The piecewise linear gray\u2010level transformation is designed to reduce the noise and to enhance the signal\u2010to\u2010noise ratio of the defective image. The second iteration segmentation based on gray range is applied to calculate the low and high thresholds, and the ternary\u2010valued defective image is acquired. The seven features calculated by Hu invariant moment and four features extracted from the ternary\u2010valued defective image are used as inputs of the conjugate gradient neural network to recognize the defect type. Experimental results show that the accuracy of the recognition reaches up to 93%<\/jats:italic>. Copyright \u00a9 2016 John Wiley & Sons, Ltd.<\/jats:p>","DOI":"10.1002\/cpe.3768","type":"journal-article","created":{"date-parts":[[2016,1,29]],"date-time":"2016-01-29T03:41:48Z","timestamp":1454038908000},"source":"Crossref","is-referenced-by-count":4,"title":["Conjugate gradient neural network\u2010based online recognition of glass defects"],"prefix":"10.1002","volume":"29","author":[{"given":"Yong","family":"Jin","sequence":"first","affiliation":[{"name":"The National Key Lab for Electronic Measurement Technology North University of China Taiyuan 030051 China"}]},{"given":"Jialiang","family":"Weng","sequence":"additional","affiliation":[{"name":"The National Key Lab for Electronic Measurement Technology North University of China Taiyuan 030051 China"}]},{"given":"Zhaoba","family":"Wang","sequence":"additional","affiliation":[{"name":"The National Key Lab for Electronic Measurement Technology North University of China Taiyuan 030051 China"}]}],"member":"311","published-online":{"date-parts":[[2016,1,28]]},"reference":[{"issue":"20","key":"e_1_2_8_2_1","first-page":"294","article-title":"Defect inspection in transparent materials","volume":"4","author":"Vanessa P","year":"2000","journal-title":"Sensor Review"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"FezaniF RahmaniA.A wavelet analysis for defects detection in flat glass. 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