Image Change Detection Algorithms: A Systematic Survey

Image Change Detection Algorithms: A Systematic Survey (2005)

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by Richard J. Radke , Srinivas Andra , Omar Al-Kofahi , Badrinath Roysam

Venue:	IEEE Transactions on Image Processing
Citations:	64 - 0 self

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Datum	Value	Source
TITLE	Image Change Detection Algorithms: A Systematic Survey	user correction - Legacy Corrections
AUTHOR NAME	Richard J. Radke	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Electrical, Computer, and Systems Engineering; Rensselaer Polytechnic Institute	user correction - Legacy Corrections
AUTHOR ADDR	110 8th Street, Troy, NY, 12180 USA	user correction - Legacy Corrections
AUTHOR NAME	Srinivas Andra	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Electrical, Computer, and Systems Engineering; Rensselaer Polytechnic Institute	user correction - Legacy Corrections
AUTHOR ADDR	110 8th Street, Troy, NY, 12180 USA	user correction - Legacy Corrections
AUTHOR NAME	Omar Al-Kofahi	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Electrical, Computer, and Systems Engineering; Rensselaer Polytechnic Institute	user correction - Legacy Corrections
AUTHOR ADDR	110 8th Street, Troy, NY, 12180 USA	user correction - Legacy Corrections
AUTHOR NAME	Badrinath Roysam	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Electrical, Computer, and Systems Engineering; Rensselaer Polytechnic Institute	user correction - Legacy Corrections
AUTHOR ADDR	110 8th Street, Troy, NY, 12180 USA	user correction - Legacy Corrections
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.	user correction - Legacy Corrections
YEAR	2005	INFERENCE
VENUE	IEEE Transactions on Image Processing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	294--307	INFERENCE
VOLUME	14	INFERENCE
CITATIONS	129 found	ParsCit 1.0