Automatic organic light-emitting diode display Mura detection model based on human visual perception and multi-resolution

Zhi Yu Zhu, Jie En Li, Po Yuan Hsieh, Jian Jia Su, Chung Hao Tien*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Organic 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.

Original languageEnglish
Title of host publicationSPIE Future Sensing Technologies
EditorsMasafumi Kimata, Christopher R. Valenta
PublisherSPIE
ISBN (Electronic)9781510631113
DOIs
StatePublished - 2019
EventSPIE Future Sensing Technologies 2019 - Tokyo, Japan
Duration: 14 Nov 2019 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11197
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Future Sensing Technologies 2019
Country/TerritoryJapan
CityTokyo
Period14/11/19 → …

Keywords

  • Contrast sensitivity function
  • Digital image processing
  • Machine vision
  • Mura
  • Oraganic light emitting diode

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