Detection of Local Intensity Changes in Grayscale Images with Robust Methods for Time-Series Analysis

Abstract

The purpose of this paper is to automatically detect local intensity changes in time series of grayscale images. For each pixel coordinate, a time series of grayscale values is extracted. An intensity change causes a jump in the level of the time series and affects several adjacent pixel coordinates at almost the same points in time. We use two-sample tests in moving windows to identify these jumps. The resulting candidate pixels are aggregated to segments using their estimated jump time and coordinates. As an application we consider data from the plasmon assisted microscopy of nanosize objects to identify specific particles in a sample fluid. Tests based on the one-sample Hodges-Lehmann estimator, the two-sample t-test or the two-sample Wilcoxon rank-sum test achieve high detection rates and a rather precise estimation of the change time.