Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhu, Zhi-Yuen_US
dc.contributor.authorLi, Jie-Enen_US
dc.contributor.authorHsieh, Po-Yuanen_US
dc.contributor.authorSu, Jian-Jiaen_US
dc.contributor.authorTien, Chung-Haoen_US
dc.date.accessioned2020-05-05T00:02:00Z-
dc.date.available2020-05-05T00:02:00Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-5106-3112-0en_US
dc.identifier.issn0277-786Xen_US
dc.identifier.urihttp://dx.doi.org/10.1117/12.2542638en_US
dc.identifier.urihttp://hdl.handle.net/11536/154069-
dc.description.abstractOrganic light emitting diode generally has serious non-uniformity phenomena due to the instability of organic processing, called Mura. In this paper, we propose an automatic Mura detection model to mimic the human perception and detect Mura pixel-wisely. First, we extract regions of interest from the original image with different sizes of windows, and then we verify these regions by SEMU criterion. Consequently, we implement human visual properties based on the contrast sensitivity function filtering and ModelFest matching to segment Mura regions. As the result, our approach can successfully detect Mura with various sizes and shapes, which could have a great impact on the display industry.en_US
dc.language.isoen_USen_US
dc.subjectMuraen_US
dc.subjectoraganic light emitting diodeen_US
dc.subjectmachine visionen_US
dc.subjectdigital image processingen_US
dc.subjectcontrast sensitivity functionen_US
dc.titleAutomatic Organic Light-emitting Diode Display Mura Detection Model based on Human Visual Perception and Multi-resolutionen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1117/12.2542638en_US
dc.identifier.journalSPIE FUTURE SENSING TECHNOLOGIESen_US
dc.citation.volume11197en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department光電工程學系zh_TW
dc.contributor.departmentDepartment of Photonicsen_US
dc.identifier.wosnumberWOS:000526177400016en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper