Adaptive sampling and monitoring of partially observed images

Abstract

Image-based monitoring techniques have achieved great success in many engineering applications. However, most existing image monitoring methods require fully observed images to implement modeling/monitoring. This requirement limits the application of these techniques for images with large size and/or cameras with limited scanning area. In this article, we propose to solve this issue by splitting the large image into multiple sub-images and using partially observed sub-images to implement monitoring of the whole large image. More specifically, the two-dimensional multivariate functional principal component analysis (2D-MFPCA) is proposed to model the cross-and-within correlation among sub-images, which facilitates the information augmentation from partially observed sub-images. The asymptotic properties of the 2D-MFPCA estimators are investigated to justify the use of MFPC scores as the monitoring statistics, which are fed into a multivariate CUSUM control chart to implement adaptive sampling and monitoring of image data. The developed chart can dynamically select locations of partially observed sub-images and adaptively focus on the most suspicious sub-images. The proposed method is validated and compared with various benchmark methods in both numerical and case studies. The results demonstrate the proposed method can achieve superior monitoring performance for large images when only partially observed sub-images are available.