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PubMed · 15762326

Image change detection algorithms: a systematic survey.

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

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AlgorithmsAnimalsArtificial IntelligenceData CollectionHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalModels, BiologicalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedSignal Processing, Computer-AssistedSubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Validation Study

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Richard J Radke, Srinivas Andra, Omar Al-Kofahi, Badrinath Roysam. 2005. Image change detection algorithms: a systematic survey.. https://doi.org/10.1109/tip.2004.838698

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