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PAMONO Sensor Data 200nm_11Apr13_2 [v2.0]

Name PAMONO Sensor Data 200nm_11Apr13_2 [v2.0]
Description

[for more information, refer to the file "documentation.txt" in the dataset "PAMONO Dataset Documentation [v2.0]"]

Stated on a high level of abstraction, the task is to identify the appearance of nano-particles (e.g. viruses) in a time-series of images. The desired signal is multiplicatively linked to a background signal of considerably larger amplitude with added noise.

A sensor model can be found in [1] (German) and in Chapter 4 of [2] (English).

A more detailed description of the task to be solved is given in [3] (English) and in Chapter 2.3 of [2] (English).

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References:
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[1] Siedhoff, D., Libuschewski, P., Weichert, F., Zybin, A., Marwedel, P., Müller, H. (2014). "Modellierung und Optimierung eines Biosensors zur Detektion viraler Strukturen" In: Bildverarbeitung für die Medizin 2014 (pp. 108-113). Springer Berlin Heidelberg.

[2] Siedhoff, D. (2016). "A parameter-optimizing model-based approach to the analysis of low-SNR image sequences for biological virus detection" PhD thesis. TU Dortmund University. DOI: http://dx.doi.org/10.17877/DE290R-17272

[3] Siedhoff, D., Fichtenberger, H., Libuschewski, P., Weichert, F., Sohler, C. and Müller, H. (2014) "Signal/Background Classification of Time Series for Biological Virus Detection" In: Pattern Recognition. Ed. by X. Jiang, J. Hornegger, and R. Koch. Vol. 8753. Lecture Notes in Computer Science. Springer Berlin Heidelberg. URL: https://link.springer.com/chapter/10.1007/978-3-319-11752-2_31

License This 200nm_11Apr13_2.zip is made available under the Open Database License: Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/odbl/
Contact Dr. Siedhoff, Dominic [Homepage ]
SFB Part Project SFB876-B2
DatasetFile 200nm_11Apr13_2.zip (1256132 KB)
Publication
Siedhoff/2016a Siedhoff, Dominic. A Parameter-Optimizing Model-Based Approach to the Analysis of Low-SNR Image Sequences for Biological Virus Detection. TU Dortmund, Dortmund, Germany, Department of Computer Science, 2016.