IEICE TRANSACTIONS on Fundamentals
A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System
Summary :
The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.6 pp.1400-1407
- Publication Date
- 2008/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e91-a.6.1400
- Type of Manuscript
- Special Section PAPER (Special Section on Image Media Quality)
- Category
Authors
Keyword
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Jong-Hwan OH, Byoung-Ju YUN, Se-Yun KIM, Kil-Houm PARK, "A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 6, pp. 1400-1407, June 2008, doi: 10.1093/ietfec/e91-a.6.1400.
Abstract: The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.6.1400/_p
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@ARTICLE{e91-a_6_1400,
author={Jong-Hwan OH, Byoung-Ju YUN, Se-Yun KIM, Kil-Houm PARK, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System},
year={2008},
volume={E91-A},
number={6},
pages={1400-1407},
abstract={The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.},
keywords={},
doi={10.1093/ietfec/e91-a.6.1400},
ISSN={1745-1337},
month={June},}
Copy
TY - JOUR
TI - A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1400
EP - 1407
AU - Jong-Hwan OH
AU - Byoung-Ju YUN
AU - Se-Yun KIM
AU - Kil-Houm PARK
PY - 2008
DO - 10.1093/ietfec/e91-a.6.1400
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2008
AB - The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Weber's Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.
ER -
English
