Abstract
Abstract
Rapid advances in imaging technology have made it possible to collect large amounts of image data in a cost‐effective manner. As a result, images are widely adopted for quality control purposes in the manufacturing industry. In image‐based quality control, images from a production process are collected over time, and the information such as product geometry or surface finish extracted from these images is used to determine whether the manufactured products satisfy the quality requirements. This is a challenging high‐velocity high‐volume big data problem. First, image streams normally generate image data at a high rate, so it is imperative to process each image quickly. Second, images often have complicated spatial structures such as edges and singularities, which render many traditional process monitoring methods inapplicable. Third, a typical image contains tens of thousands of pixels, so the data is high‐dimensional. It has been shown in the literature that conventional multivariate control charts have limited power of detecting process shifts when the data dimension is high. In this expository article, we divide the image monitoring applications into two categories: (i) images with deterministic features and (ii) images with stochastic features. We introduce representative methods in the two categories and discuss their potential to solve the problems in image monitoring. Some recent research in color image monitoring is discussed as well. Suggestions for future research and possible applications of image monitoring methods beyond industrial quality control are given in the end.