A New Approach to Detection of Abrupt Changes in Black-and-White Images

摘要

In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. Nowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. There is a variety of data types: texts, ordinary numbers, images, audio or video files or streams, metadata descriptions, etc. All of them change in many ways. If the change happens then the interesting issue is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change in pictures. Many algorithms have been proposed to detect abnormalities and deviations in the data. We propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to one- and two-dimensional patterns to detect the abrupt changes.