Direct Matrix Factorization and Alignment Refinement: Application to Defect Detection

Abstract

Abstract-Defect detection approaches based on template differencing require precise alignment of the input and template image; however, such alignment is easily affected by the presence of defects. Often, non-trivial pre/post-processing steps and/or manual parameter tuning are needed to remove false alarms, complicating the system and hampering automation. In this work, we explicitly address alignment and defect extraction jointly, and provide a general iterative algorithm to improve both their performance to pixel-wise accuracy. We achieve this by utilizing and extending the robust rank minimization and alignment method of [12]. We propose an effective and efficient optimization algorithm to decompose a template-guided image matrix into a low-rank part relating to alignment-refined defect-free images and an explicit error component containing the defects of interest. Our algorithm is fully automatic, training-free, only needs trivial pre/postprocessing procedures, and has few parameters. The rank minimization formulation only requires a linearly correlated template image, and a template-guided approach relieves the common assumption of small defects, making our system very general. We demonstrate the performance of our novel approach qualitatively and quantitatively on a real-world dataset with defects of varying appearance.