Abstract
The purpose of this paper is to automatically detect local intensity changes in time series of grayscale images. For each pixel coordinate, a time series of grayscale values is extracted. An intensity change causes a jump in the level of the time series and affects several adjacent pixel coordinates at almost the same points in time. We use two-sample tests in moving windows to identify these jumps. The resulting candidate pixels are aggregated to segments using their estimated jump time and coordinates. As an application we consider data from the plasmon assisted microscopy of nanosize objects to identify specific particles in a sample fluid. Tests based on the one-sample Hodges-Lehmann estimator, the two-sample t-test or the two-sample Wilcoxon rank-sum test achieve high detection rates and a rather precise estimation of the change time.
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Acknowledgments
The work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project C3. The computations were performed on the LiDO HPC cluster at TU Dortmund University. We thank the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. in Dortmund for providing the PAMONO data to us and Dominic Siedhoff and Pascal Libuschewski for helpful advice on how to work with the PAMONO data and for providing the results for the reference method.
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Abbas, S., Fried, R., Gather, U. (2016). Detection of Local Intensity Changes in Grayscale Images with Robust Methods for Time-Series Analysis. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_13
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