Name
Abstract | ||
|---|---|---|
Visual defects, called mura in the field, sometimes occur during the manufacturing of the flat panel liquid crystal displays. In this paper we propose an automatic inspection method that reliably detects and quantifies TFT-LCD region-mura defects. The method consists of two phases. In the first phase we segment candidate region-muras from TFT-LCD panel images using the modified regression diagnostics and Niblack's thresholding. In the second phase, based on the human eye's sensitivity to mura, we quantify mura level for each candidate, which is used to identify real muras by grading them as pass or fail. Performance of the proposed method is evaluated on real TFT-LCD panel samples. |
Year | Venue | Keywords |
|---|---|---|
2004 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | machine vision, image segmentation, regression diagnostics, industrial inspection, visual perception |
Field | DocType | Volume |
Computer vision,Thin-film transistor,Mura,Computer graphics (images),Machine vision,Computer science,Image segmentation,Liquid-crystal display,Artificial intelligence,Industrial inspection,Visual perception | Journal | E87D |
Issue | ISSN | Citations |
10 | 0916-8532 | 34 |
PageRank | References | Authors |
4.04 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Yeong Lee | 1 | 34 | 4.71 |
| Suk In Yoo | 2 | 43 | 6.14 |