Abstract
We consider the problem to monitor a sequence of images that may be affected by spatial as well as temporal dependencies. In order to detect a change, we consider a detector based on linear combinations of quadratic forms, thus allowing to consider linear contrasts of subimages in terms of their average grey value. We derive the asymptotic distribution of the proposed detector and the underlying empirical processes under the no-change null hypothesis and general alternatives.
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Acknowledgements
The authors acknowledge the support of BMWi (Federal Ministry for Economic Affairs and Energy) under grant No. 0325588B, PV-Scan project.
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© 2015 Springer International Publishing Switzerland
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Prause, A., Steland, A. (2015). Detecting Changes in Spatial-Temporal Image Data Based on Quadratic Forms. In: Steland, A., Rafajłowicz, E., Szajowski, K. (eds) Stochastic Models, Statistics and Their Applications. Springer Proceedings in Mathematics & Statistics, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-319-13881-7_16
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DOI: https://doi.org/10.1007/978-3-319-13881-7_16
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