Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyMacariniWebe:2017:QuCoSy
TitleQuality Control System for Ceramic Tiles using Segmentation-based Fractal Texture Analysis and SVM
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size429 KiB
Context area
Author1 Macarini, Luiz Antonio
2 Weber, Tiago Oliveira
Affiliation1 Universidade Federal de Santa Catarina
2 Universidade Federal de Santa Catarina
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeIndustry Application Paper
History2017-08-29 01:15:25 :: -> administrator ::
2020-02-20 22:06:46 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
KeywordsImage Processing, Machine Learning, SFTA, Ceramic Tiles, Defect Detection.
AbstractThe ceramic industry has a highly automated production system. The quality control, however, is still performed by humans, which limits its speed and precision. This work proposes a complete verification system for ceramic tiles based on image processing and machine learning. The system has four steps: image acquisition, pre-processing, feature extraction and classification. The feature extraction step uses Segmentation-based Fractal Texture Analysis (SFTA). A Support Vector Machine is employed to classify the ceramic tiles. The system is implemented using OpenCV libraries. In total, 783 ceramic tiles were used, being 80% for training and 20% to testing. The present work had reached the proposed objectives, both in processing time and accuracy, achieving 98.68% of detection rate.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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