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Conference Paper: Non-concentric Circular Texture Removal for Workpiece Defect Detection

TitleNon-concentric Circular Texture Removal for Workpiece Defect Detection
Authors
Issue Date2019
PublisherSpringer. The Proceedings' web site is located at https://link.springer.com/conference/icira
Citation
12th International Conference on Intelligent Robotics and Applications (ICIRA) 2019: Intelligent Robotics and Applications, Shenyang, China, 8-11 August 2019, pt. 4, p. 576-584 How to Cite?
AbstractSince workpiece defect detection is a typical problem in computer vision with small datasets, generally its solutions cannot exploit the advantages of high accuracy, generalization ability, and neural network structures from the deep learning paradigm. Thus, traditional image processing techniques are still widely applied in such requirements. Aiming at three types of defects (crack, pitting and scratch) on a workpiece with non-concentric circular textures that severely interfere in the defect recognition stage, this paper proposes a sliding window filter for the texture detection. Experiments compare the proposed method with the polar coordinate mapping method and the T-smooth texture removal algorithm. Results show that the proposed method reveals the three types of defects better than the other two methods.
Persistent Identifierhttp://hdl.handle.net/10722/282979
ISBN
Series/Report no.Lecture Notes in Computer Science (LNCS) ; v. 11743

 

DC FieldValueLanguage
dc.contributor.authorQin, S-
dc.contributor.authorGuo, D-
dc.contributor.authorChen, H-
dc.contributor.authorXi, N-
dc.date.accessioned2020-06-05T06:23:43Z-
dc.date.available2020-06-05T06:23:43Z-
dc.date.issued2019-
dc.identifier.citation12th International Conference on Intelligent Robotics and Applications (ICIRA) 2019: Intelligent Robotics and Applications, Shenyang, China, 8-11 August 2019, pt. 4, p. 576-584-
dc.identifier.isbn978-3-030-27537-2-
dc.identifier.urihttp://hdl.handle.net/10722/282979-
dc.description.abstractSince workpiece defect detection is a typical problem in computer vision with small datasets, generally its solutions cannot exploit the advantages of high accuracy, generalization ability, and neural network structures from the deep learning paradigm. Thus, traditional image processing techniques are still widely applied in such requirements. Aiming at three types of defects (crack, pitting and scratch) on a workpiece with non-concentric circular textures that severely interfere in the defect recognition stage, this paper proposes a sliding window filter for the texture detection. Experiments compare the proposed method with the polar coordinate mapping method and the T-smooth texture removal algorithm. Results show that the proposed method reveals the three types of defects better than the other two methods.-
dc.languageeng-
dc.publisherSpringer. The Proceedings' web site is located at https://link.springer.com/conference/icira-
dc.relation.ispartofInternational Conference on Intelligent Robotics and Applications (ICIRA)-
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS) ; v. 11743-
dc.titleNon-concentric Circular Texture Removal for Workpiece Defect Detection-
dc.typeConference_Paper-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.identifier.doi10.1007/978-3-030-27538-9_49-
dc.identifier.hkuros310079-
dc.identifier.volumept. 4-
dc.identifier.spage576-
dc.identifier.epage584-
dc.publisher.placeCham-

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