%0 Journal Article %1 1395984 %A Radke, R.J. %A Andra, S. %A Al-Kofahi, O. %A Roysam, B. %D 2005 %J Image Processing, IEEE Transactions on %K ComputerVision algorithms data_analysis motion_analysis optical_flow registration survey to_READ %N 3 %P 294-307 %R 10.1109/TIP.2004.838698 %T Image change detection algorithms: a systematic survey %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1395984 %V 14 %X 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.

@article{1395984, 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.}, added-at = {2013-04-05T13:08:41.000+0200}, author = {Radke, R.J. and Andra, S. and Al-Kofahi, O. and Roysam, B.}, biburl = {https://www.bibsonomy.org/bibtex/2d94a61d699ba5b541321874782b15606/alex_ruff}, description = {IEEE Xplore - Image change detection algorithms: a systematic survey}, doi = {10.1109/TIP.2004.838698}, interhash = {90acf1445d8f4cff6ac0d40565c610a5}, intrahash = {d94a61d699ba5b541321874782b15606}, issn = {1057-7149}, journal = {Image Processing, IEEE Transactions on}, keywords = {ComputerVision algorithms data_analysis motion_analysis optical_flow registration survey to_READ}, number = 3, pages = {294-307}, timestamp = {2013-04-05T13:23:22.000+0200}, title = {Image change detection algorithms: a systematic survey}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1395984}, volume = 14, year = 2005 }