Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW (2016)
PLOS ONE 11(6): e0157329.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors.
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PLOS ONE
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11
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6
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e0157329
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Osterloff J, Nilssen I, Eide I, de Oliveira Figueiredo MA, de Souza Tâmega FT, Nattkemper TW. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 2016;11(6): e0157329.
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M. A., de Souza Tâmega, F. T., & Nattkemper, T. W. (2016). Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE, 11(6), e0157329. doi:10.1371/journal.pone.0157329
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M. A., de Souza Tâmega, F. T., and Nattkemper, T. W. (2016). Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE 11:e0157329.
Osterloff, J., et al., 2016. Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE, 11(6): e0157329.
J. Osterloff, et al., “Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation”, PLOS ONE, vol. 11, 2016, : e0157329.
Osterloff, J., Nilssen, I., Eide, I., de Oliveira Figueiredo, M.A., de Souza Tâmega, F.T., Nattkemper, T.W.: Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation. PLOS ONE. 11, : e0157329 (2016).
Osterloff, Jonas, Nilssen, Ingunn, Eide, Ingvar, de Oliveira Figueiredo, Marcia Abreu, de Souza Tâmega, Frederico Tapajós, and Nattkemper, Tim Wilhelm. “Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation”. PLOS ONE 11.6 (2016): e0157329.
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44 References

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Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.
Schoening T, Bergmann M, Ontrup J, Taylor J, Dannheim J, Gutt J, Purser A, Nattkemper TW., PLoS ONE 7(6), 2012
PMID: 22719868
Increase of litter at the Arctic deep-sea observatory HAUSGARTEN.
Bergmann M, Klages M., Mar. Pollut. Bull. 64(12), 2012
PMID: 23083926
A computer vision approach for monitoring the spatial and temporal shrimp distribution at the LoVe observatory
AUTHOR UNKNOWN, 2016
Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation.
Beijbom O, Edmunds PJ, Roelfsema C, Smith J, Kline DI, Neal BP, Dunlap MJ, Moriarty V, Fan TY, Tan CJ, Chan S, Treibitz T, Gamst A, Mitchell BG, Kriegman D., PLoS ONE 10(7), 2015
PMID: 26154157
Hyperbolic Self-Organizing Maps for Semantic Navigation
AUTHOR UNKNOWN, 2002

AUTHOR UNKNOWN, 2015
Fully automated image segmentation for benthic resource assessment of poly-metallic nodules
AUTHOR UNKNOWN, 2016

AUTHOR UNKNOWN, 1978

AUTHOR UNKNOWN, 2010

AUTHOR UNKNOWN, 0
Comparative study of Hough Transform methods for circle finding
AUTHOR UNKNOWN, 1990

AUTHOR UNKNOWN, 2007

AUTHOR UNKNOWN, 2012

AUTHOR UNKNOWN, 2007
Cross-Validatory Estimation of the Number of in Factor and Principal Components Components Models
AUTHOR UNKNOWN, 1978

AUTHOR UNKNOWN, 2002

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 2009

AUTHOR UNKNOWN, 0

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