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摘要当今社会，显示技术突飞猛进。LCD则是其中材料的佼佼者。其显示性能优越，性价比在近年也是不断提高中。电子行业的突飞猛进，各大公司竞相发展液晶产业。于是液晶生产各环节便成了重中之重。
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国内LCD玻璃基板检测多用人工检测的方式。但随着国内人工成本的不断上升，其成本过高，效率较低，不适合大规模发展。
本文阐述了一种基于图像处理的液晶玻璃基板的自动检测技术。通过三大环节：图像采集，图像预处理，图像检测。来达到液晶玻璃基板质量的检测与裂痕污点的分辨。图像采集时选用了LED光源与CMOS摄像器件。图像预处理对图像进行了图像的灰度化处理，中值滤波，阈值分割与边缘平滑，便于进行下一步的检测。在最后的图像检测过程中，运用了“圆形度”的形状特征概念，来区分裂痕与污点，达到了较好的效果。全过程中选用了MATLAB软件，以便于优化实施相关过程方法。在图样上检测效果较好。20980
关键词 图像处理 自动检测 MATLAB
毕业论文设计说明书（论文）外文摘要
Title Based on image processing of the LCD substrate cracks automatic detection research
Abstract
Today's society, display technology by leaps and bounds. LCD is one of the best materials. Its shows superior performance, cost-effective in recent years is also increasing. The rapid development of electronic industry, companies compete to development of LCD industry. So the LCD production each link has become top priority.
The LCD glass substrate inspection use artificial way. But with the rising of domestic labor costs, the cost is too high, low efficiency, not suitable for large-scale development.
This paper expounds a kind of based on image processing of the LCD glass substrate automatic detection technology. Through the three links: image acquisition, image preprocessing, image detection. To achieve LCD glass substrate quality detection and crack stain. The LED light source at the beginning of image acquisition and CMOS camera device. Image pre-processing on the image gray level of median filter, threshold segmentation and edge smooth, easy to take the next step of detection. In the process of the final image detection, using the concept of the shape characteristics of the "circular", to distinguish between cracks and stain, achieved good results. In use in the whole process of the MATLAB software, in order to optimize the implementation of the related process method. On the pattern detection effect is better.
Keywords image processing,image detection,MATLAB
目次
1. 绪论 1
1.1 LCD的构成及裂痕对显示品质的影响。 1
1.2 传统的液晶玻璃基板裂痕检测方法 1
1.3数字图像处理技术 3
1.4 MATLAB技术 4
1.5 主要设计思路 4
2. 液晶基板表面裂痕检测总体设计方案。 4
3. 图像采集 5
3.1原理 5
3.2光源的选择与方案设计 6
4. 图像预处理 7
4.1图像类型的转换。 8
4.2 图像的中值滤波 9
4.3阈值分割 11
4.4 边缘平滑 12
5.图像检测 15
5．1概念介绍 16
5.2几何特征 17
5.3形状特征 17
5.4 算法实现 18
5.自动检测系统整体模块设计 19