Surface defects detection for fired ceramic tiles using Monochrome and Color image processing analysis

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dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorM. Elbehiery, Hussam
dc.contributor.authorAbdelmouez M., Ghada
dc.contributor.authorA. Hefnawy, Alaa
dc.contributor.authorTarek Elewa, M.
dc.date.accessioned2020-02-24T09:36:36Z
dc.date.available2020-02-24T09:36:36Z
dc.date.issued2005
dc.descriptionMSA Google Scholaren_US
dc.description.abstractOne of many applications of vision systems is quality control. Quality control in ceramic tile manufacturing is hard, labor-intensive operation. Ceramic tiles classification depends on three main factors color analysis, dimension verification, and surface defects. Our work introduces enhanced algorithms to detect the color and surface defects in the fired ceramic tiles using principles of image processing analysis. This algorithm assumed as a visual inspection system that helps in the sorting operation before packing operation to improve the homogeneity of batches received by costumer.en_US
dc.identifier.citation[1]Vincent LEBRUN, ”Quality control of ceramic tiles by machine vision,” Flaw Master 3000, Surface Inspection Ltd. 2001. [2]Stewart Coe, “Automatic tile inspection.” Surface Inspection Limited, International Ceramics, Bristol, U.K., Issue 1, 2000. [3] Costas Boukouvalas, Francesco De Natale, Josef Kittler, and Roberto Salgrai. “An Integrated system for Quality Inspection of tiles.” University of Surrey, Guilford, GU2-5XH, England, 1999. [4] Dipl. Ing, Dipl. Kfm, Horst Jonuscheit, Joachim Wagner,” Fully automated inspection system for clay roofing tiles.” Zi Ziegelindustrie International 11/98, pages 741 – 751, Bauverlag, Walluf, Germany, 1998. [5] Yonghuai Liu and Macros Rodrigues, “A novel machine vision algorithm for a fast response quality control system.” Department of Computer Science, the University of Hull, Hull, HU6 7RX, UK 2001. [6] Richard Bridge, “Computer Image Processing.” Tessella support services PLC, Oxon, England, Issue V1.R2.M0, June 2003. [7] Simon Baker, “Design and Evaluation of Feature Detectors.” Doctor of philosophy thesis in the Graduate School of Arts and Sciences, Columbia University, 1998. [8] Song K Y, Petrou M, and Kittler, “Texture crack detection.” Machine Vision Applications, Jan., 1995. [9] Kennedy Behrman, " CIS 700/010: Matrix Operations I." Suresh Venkatasubramanian, USA, March 3, 2005. [10] Paul Haeberli, "Matrix Operations for Image Processing." A Publication of GRAFICA Obscura, USA, November 1993. [11] Mark E. Lehr and Keh-Shin Li, “Template basis techniques to Pattern recognition.” University of California Riverside, Department of Statistics Riverside, California, USA, Vol. 2825, 1996. [12] Robert Nowak, and Clayton Scott, ”Template Learning from Atomic Representation: A Wavelet-based Approach to Pattern Analysis,” Department of Electrical and Computer Engineering, MS366, Rice University, Houston, TX 77005, Apr. 2001. [13]C. Boukouvalas, J Kittler, R Marik, M Mirmehdi and M Petrou, “Ceramic Tile Inspection for Color and Structural defects.” I.E.E.E. Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, March 1998. [14] Martin Doyle, “Control your Color.” Marketing Manager, Type maker Ltd., Birmingham, UK 9th January 2001. [15] J. Kittler, R. Marik, M Petrou, J Song,” Detection of defects in color texture surfaces.” University of Surrey, Guildford GU2 5XH, UK, 1993.en_US
dc.identifier.urihttps://t.ly/NXXqe
dc.language.isoenen_US
dc.publisherEconomics Working Paper Archive-EconWPA, Industrial Organizationen_US
dc.relation.ispartofseriesEconomics Working Paper Archive-EconWPA, Industrial Organization;number 0510008
dc.subjectQuality controlen_US
dc.subjectclassificationen_US
dc.subjectcolor analysisen_US
dc.subjectsurface defecten_US
dc.subjectvisual inspectionen_US
dc.titleSurface defects detection for fired ceramic tiles using Monochrome and Color image processing analysisen_US
dc.typeArticleen_US

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