| Summary: | 碩士 === 健行科技大學 === 工業管理系碩士班 === 106 === Mura is a visual defect due to non-uniformity on the surface of the backlight layer of TFT-LCD products. Now, mura can be identified and photted by automatic optical inspection system, however, it is still necessary for a lot of manpower to classify mura pictures into several clusters. Since the perceived differences between the inspectors, the classification results are easily inconsistent. Moreover, visual fatigue is likely to make the missing rate and false rate increase. Hence, the objective of this study is to develop the automatic mura classification algorithm by its several geometric characteristics. At first, we propose ten geometric characteristics of a mura body: size of mura, perimeter, maximum projective range, minimum projective range, splash, the average of grey level luminance, the standard deviation of grey level luminance, the skewness of grey level luminance, the proportion of within variation of two stratified muras, and the ratio of two stratified muras. Secondly, we use decision tree method to classify 188 real mura pictures into five clusters to investigate if the proposed geometric characteristic variables are adequate. There are five proposed geometric characteristics remained in the classification algorithm, the catch rate and accurate rate are 94.21% and 93.57%, respectively. Finally, we investigate the performance of the decision tree algorithm by the stratified cross validation method. After omitting 11 error classified pictures, the averages of catch rate and accurate rate in 1000 replications are 96.52% and 96.57%, respectively.
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