The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics

Matuttis, Hans-Georg; Holler, Silvia; Casiraghi, Federica; Schneider, Johannes Josef; Faggian, Alessia; Füchslin, Rudolf Marcel; Hanczyc, Martin Michael

doi:10.1007/978-3-031-23929-8_5

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics
Authors:	Matuttis, Hans-Georg Holler, Silvia Casiraghi, Federica Schneider, Johannes Josef Faggian, Alessia Füchslin, Rudolf Marcel Hanczyc, Martin Michael
et. al:	No
DOI:	10.1007/978-3-031-23929-8_5
Proceedings:	Artificial Life and Evolutionary Computation
Page(s):	51
Pages to:	62
Conference details:	XV International Workshop on Artificial Life and Evolutionary Computation (WIVACE), Winterthur, Switzerland, 15-17 September 2021
Issue Date:	22-Jan-2023
Series:	Communications in Computer and Information Science
Series volume:	1722
Publisher / Ed. Institution:	Springer
Publisher / Ed. Institution:	Cham
ISBN:	978-3-031-23928-1 978-3-031-23929-8
Language:	English
Subjects:	Image processing; Hough transform; Cluster of droplets in fluids
Subject (DDC):	540: Chemistry
Abstract:	We explain how to optimize the image analysis of mixed clusters of red and green droplets in solvents with various degrees of sharpness, brightness, contrast and density. The circular Hough Transform is highly efficient for separated circles with reasonable background contrast, but not for large amounts of partially overlapping shapes, some of them blurred, as in the images of our dense droplet suspensions. We explain why standard approaches for image improvement fail and present a “shootout” approach, where already detected circles are masked, so that the removal of sharp outlines improves the relative optical quality of the remaining droplets. Nevertheless, for intrinsic reasons, there are limits to the accuracy of data which can be obtained on very dense clusters.
URI:	https://digitalcollection.zhaw.ch/handle/11475/27307
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Published as part of the ZHAW project:	ACDC – Artificial Cells with Distributed Cores to Decipher Protein Function
Appears in collections:	Publikationen School of Engineering

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Matuttis, H.-G., Holler, S., Casiraghi, F., Schneider, J. J., Faggian, A., Füchslin, R. M., & Hanczyc, M. M. (2023). The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics [Conference paper]. Artificial Life and Evolutionary Computation, 51–62. https://doi.org/10.1007/978-3-031-23929-8_5

Matuttis, H.-G. et al. (2023) ‘The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics’, in Artificial Life and Evolutionary Computation. Cham: Springer, pp. 51–62. Available at: https://doi.org/10.1007/978-3-031-23929-8_5.

H.-G. Matuttis et al., “The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics,” in Artificial Life and Evolutionary Computation, Jan. 2023, pp. 51–62. doi: 10.1007/978-3-031-23929-8_5.

MATUTTIS, Hans-Georg, Silvia HOLLER, Federica CASIRAGHI, Johannes Josef SCHNEIDER, Alessia FAGGIAN, Rudolf Marcel FÜCHSLIN und Martin Michael HANCZYC, 2023. The good, the bad and the ugly : droplet recognition by a “shootout”-heuristics. In: Artificial Life and Evolutionary Computation. Conference paper. Cham: Springer. 22 Januar 2023. S. 51–62. ISBN 978-3-031-23928-1

Matuttis, Hans-Georg, Silvia Holler, Federica Casiraghi, Johannes Josef Schneider, Alessia Faggian, Rudolf Marcel Füchslin, and Martin Michael Hanczyc. 2023. “The Good, the Bad and the Ugly : Droplet Recognition by a “Shootout”-Heuristics.” Conference paper. In Artificial Life and Evolutionary Computation, 51–62. Cham: Springer. https://doi.org/10.1007/978-3-031-23929-8_5.

Matuttis, Hans-Georg, et al. “The Good, the Bad and the Ugly : Droplet Recognition by a “Shootout”-Heuristics.” Artificial Life and Evolutionary Computation, Springer, 2023, pp. 51–62, https://doi.org/10.1007/978-3-031-23929-8_5.