Massively parallel change detection with application to visual quality control

Abstract

Our aim in this paper is to extend the results on parallel change detection recently discussed in [15], where such a detector has been proposed. Here, we emphasize its adaptive abilities to follow changing background and relax some theoretical assumptions on random errors, extending possible applications of the detector. We also discuss its implementation in NVidia CUDA technology and provide results of its extensive testing when applied to copper visual quality control, which is a challenge due to the need for massively parallel calculations in real-time.