TY - GEN
T1 - A Region-Scalable Fitting Model Algorithm Combining Gray Level Difference of Sub-image for AMOLED Defect Detection
AU - Sun, Yufeng
AU - Xiao, Junjun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/20
Y1 - 2018/11/20
N2 - In this paper, we improve the Region-Scalable Fitting (RSF) model by the gray level difference of sub-image for RSF model's sensitivity to initial contour and slow speed in active matrix organic light emitting diode (AMOLED) defect detection. The gray level difference of sub-image algorithm can only locate the approximate defects area. The Region-Scalable Fitting (RSF) model overcomes the detection difficulty caused by the intensity inhomogeneity, but the local characteristic makes it extremely sensitive to the position of the initial contour curve. To solve this problem, we combine the gray level difference of sub-image algorithm with the RSF model. Firstly, the approximate position of the defects area is found by the gray level difference of sub-image algorithm, and the outline of this approximate defects area is taken as the initial contour curve of the RSF model. Then we implement the RSF model to segment the defects accurately. The experimental results show that the use of gray level difference of sub-image algorithm to locate the initial contour overcomes the disadvantage that the RSF model is sensitive to the initial contour, and improves the detection speed.
AB - In this paper, we improve the Region-Scalable Fitting (RSF) model by the gray level difference of sub-image for RSF model's sensitivity to initial contour and slow speed in active matrix organic light emitting diode (AMOLED) defect detection. The gray level difference of sub-image algorithm can only locate the approximate defects area. The Region-Scalable Fitting (RSF) model overcomes the detection difficulty caused by the intensity inhomogeneity, but the local characteristic makes it extremely sensitive to the position of the initial contour curve. To solve this problem, we combine the gray level difference of sub-image algorithm with the RSF model. Firstly, the approximate position of the defects area is found by the gray level difference of sub-image algorithm, and the outline of this approximate defects area is taken as the initial contour curve of the RSF model. Then we implement the RSF model to segment the defects accurately. The experimental results show that the use of gray level difference of sub-image algorithm to locate the initial contour overcomes the disadvantage that the RSF model is sensitive to the initial contour, and improves the detection speed.
KW - AMOLED
KW - RSF model
KW - defects
KW - gray level difference of sub-image
UR - https://www.scopus.com/pages/publications/85059773464
U2 - 10.1109/CCET.2018.8542361
DO - 10.1109/CCET.2018.8542361
M3 - 会议稿件
AN - SCOPUS:85059773464
T3 - 2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018
SP - 300
EP - 304
BT - 2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018
Y2 - 18 August 2018 through 20 August 2018
ER -