Title: Image Change Detection Algorithms: A Systematic Survey
Authors: Richard J. Radke, Srinivas Andra, Omar Al-Kofahi, and Badrinath Roysam

Speaker: Cuizhu Shi
Location: 2222 Coover
Time: Thursday 2:10-3

Abstract: Detecting regions of change in multiple images of the same scene
taken at different times is of widespread interest due to a large number
of applications in diverse disciplines, including remote sensing,
surveillance, medical diagnosis and treatment, civil infrastructure, and
underwater sensing. This paper presents a systematic survey of the common
processing steps and core decision rules in modern change detection
algorithms, including significance and hypothesis testing, predictive
models, the shading model, and background modeling. We also discuss
important preprocessing methods, approaches to enforcing the consistency
of the change mask, and principles for evaluating and comparing the
performance of change detection algorithms. It is hoped that our
classification of algorithms into a relatively small number of categories
will provide useful guidance to the algorithm designer.