| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 96 === The products which required automatic visualinspection are increasing dramatically through the years. It is necessary to develop different inspection methods for various type of products. In addition, every inspection method development is very time consuming; they can only inspect a single product at a time,which is very inefficient. Therefore, this research intents to develop an automatic visual inspection method for inspecting the products which contain the repeated periodic texture characteristic.
The adaptive subtraction method is a defects-checker algorithm that doesn’t need golden sample image. It mainly utilizes correlation coefficient method to rapidly seek the period of image feature, and add a filter on the traditional subtraction to overcome the problems of rotating and displacing of images. First, the signal of 1-D gray-level line image is retrieved by using a product image. Secondly, a correlation coefficient method is used to check the periodicity of the periodic feature. Thirdly, the gray-level differences between the pixels at the same place of the two adjacent periodicities are calculated. Finally, by setting a threshold we can differentiate these defects. This research is mainly designed to detect the surface image defects of two types of LCD panels, three types of color filters, casting samples, and fabrics which all possess the periodic textures characteristic. And according to the experiment results of these seven samples which have total of 268 images, the accuracy rate of this method can reaches 91.79%. It can achieve a fast computation of 0.125 seconds for a 640× 450 image. Therefore, this method can be applied for detecting defects of products with periodic textures characteristic.
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