[Paper] Development of System to Classify Speckle Images for Visual Inspection of Cutlery

This paper develops a system to visually inspect cutlery based on a simple machine learning algorithm using image features that are robust against overexposure. First, we develop an image acquisition apparatus comprising a laser and a screen that produces speckle images of unique shapes depending on the degree to which the photographed cutlery has been polished. The contribution of this study is to produce speckle images in this way. This enables accurate classification without newly deriving a sophisticated machine learning algorithm in the subsequent processing. We use the speckle images to develop moment-related features that represent the unique shapes and avoid the problem of overexposure. Second, we apply the extreme learning machine, a simple but representative machine learning algorithm, to the obtained features. Experimental results using real cutlery show that our developed system achieved good accuracy and precision regardless of exposure time.