A cascaded Mura defect detection method based on mean shift and level set algorithm for active‐matrix OLED display panel. Issue 1 (13th November 2018)

• Record Type: Journal Article
• Authors: Sun, Yufeng; Li, Xiongjie; Xiao, Junjun
• Is Part Of: Journal of the Society for Information Display. Volume 27:Issue 1(2019)
• Page Start: 13
• Page End: 20
• Publication Date: 2018-11-13
• Subjects: detection -- level set -- mean shift -- Mura; Information display systems -- Periodicals; 621.38154205
• DOI: 10.1002/jsid.743
• Languages: English
• ISSNs: 1071-0922

Abstract

Abstract: Active‐matrix OLED (AMOLED), as the next‐generation display technology, is being commercially promoted rapidly. And Mura defects occur unavoidable during different phases of the AMOLED panel production process. In this paper, a cascaded Mura detection method leveraging the mean shift and the level set algorithm is proposed. First, we use the mean shift algorithm to find the general contour of the Mura defects that circumvent the issue in established level set segmentation approach wherein the use of the local image information is sensitive to the initial contour. Then, we improve the level set model that combines global and local information to segment Mura defects accurately. The integration of local image information can levitate the challenges in the global image model, which cannot separate the local intensity inhomogeneity and texture background individually. The experiments show that the cascaded method has a superior capability in terms of both accuracy and efficiency. Abstract : A Mura detection method for active‐matrix organic LED (AMOLED) leveraging mean shift and level set algorithm is proposed. First, we use mean shift algorithm to find the contour of the Mura defects that circumvent the issue that level set approach is sensitive to the initial contour. Then, we improve level set model that combines global and local information to segment Mura defects accurately.