Separating an Outlier from a Change

Abstract

We study the quickest change detection problem with an unknown post-change distribution. In this scenario, the unknown change in the distribution of observations may occur in many ways without much structure, while, before change, an outlier (a false alarm event) is highly structured, following a particular sample path. We first characterize these likely events for the deviation of finite strings and propose a method to test the deviation, relative to the most likely way for it to occur as an outlier. Our method works along with other change detection schemes to substantially reduce the false positive rates associated with the plain scheme used without the heavy computation associated with the generalized likelihood ratio test. Finally, we apply our method on economic market indicators and climate data. Our method successfully captures the regime shifts during times of historical significance and identifies the current climate change phenomenon to be a highly likely regime shift.

Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology

“…To evaluate the enhancement effect further, five algorithms, i.e., CLAHE, histogram equalization (HE) algorithm [31], AHE algorithm [31], single-scale Retinex (SSR) algorithm [32], brightness compensation method for different brightness regions in the image (BCMDBR), and homomorphic filtering [33] are compared in this section. The results are shown in Figure 5.…”