{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:53:22Z","timestamp":1777704802123,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:00:00Z","timestamp":1590451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,7,17]]},"abstract":"<jats:p>Automated visual inspection is becoming an important field of computer vision in many industries. The real-time inspection of flat surface products is a task full of challenges in industrial aspects that requires fast and accurate algorithms for detection and localisation of defects. Structural, statistical and filter-based approaches, such as Gabor Filter Banks, Log-Gabor filter and Wavelets, have high computational complexity.<\/jats:p>\n                  <jats:p>This paper introduces a fast and accurate model for inspection and localization of industrial flat surface products: Neighborhood Preserving Perceptual Fidelity Aware Mean Squared Error (NP-PAMSE). The Extreme Learning Machine (ELM) is used for classification. ELM is found to be the perfect classifier for detecting defects. The proposed model resulted in defect detection accuracy of 99.86%, with 98.16% sensitivity, and 99.90% specificity.<\/jats:p>\n                  <jats:p>These results show that the proposed model outperforms many existing defect detection approaches. The discriminant power displays the efficiency of ELM in differentiation between normal and abnormal surfaces.<\/jats:p>","DOI":"10.3233\/jifs-192071","type":"journal-article","created":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T11:16:09Z","timestamp":1590491769000},"page":"1183-1196","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Neighborhood preserving perceptual fidelity aware MSE for visual inspection of industrial flat surface products"],"prefix":"10.1177","volume":"39","author":[{"given":"Mustafa","family":"Ameen","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt"}]},{"given":"Mohammed","family":"Alrahmawy","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt"}]},{"given":"Amal","family":"AbouEleneen","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt"}]},{"given":"Ahmad","family":"Tolba","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, Mansoura University, Egypt"}]}],"member":"179","published-online":{"date-parts":[[2020,5,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/systems7010007"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"XueW. 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