@inproceedings{91780da215de44eebdfbadae66bd138d,
title = "Automatic organic light-emitting diode display Mura detection model based on human visual perception and multi-resolution",
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.",
keywords = "Contrast sensitivity function, Digital image processing, Machine vision, Mura, Oraganic light emitting diode",
author = "Zhu, {Zhi Yu} and Li, {Jie En} and Hsieh, {Po Yuan} and Su, {Jian Jia} and Tien, {Chung Hao}",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 SPIE.; SPIE Future Sensing Technologies 2019 ; Conference date: 14-11-2019",
year = "2019",
doi = "10.1117/12.2542638",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masafumi Kimata and Valenta, {Christopher R.}",
booktitle = "SPIE Future Sensing Technologies",
address = "美國",
}