RAPID: Research on Automated Plankton Identification

Benfield, Mark C.; Grosjean, Philippe; Culverhouse, Phil F.; Irigoien, Xabier; Sieracki, Michael E.; López‐Urrutia, Ángel; Dam, Hans G.; Qiao, Hui; Davis, Cabell S.; Hansen, Allen; Pilskaln, Cynthia H.; Riseman, Edward M.; Schultz, Howard; Utgoff, Paul E.; Gorsky, Gabriel

doi:10.5670/oceanog.2007.63

Cited by 238 publications

(187 citation statements)

References 31 publications

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“…Also if new software is created, it would be interesting to see how the performance differs from the existing software. Maintenance of software by the marine ecology community is essential, where plankton classification software [4] can be seen as a positive example. Maintenance of data is also essential, because modern recognition software often learns from large datasets of images, where new challenges in domain specific image recognition [20] can bring better methodologies.…”

Section: Discussionmentioning

confidence: 99%

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A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Boom

Palazzo

et al. 2014

Ecological Informatics

121

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We present a research tool that supports marine ecologists' research by allowing analysis of long-term and continuous fish monitoring video content. The analysis can be used for instance to discover ecological phenomena such as changes in fish abundance and species composition over time and area. Two characteristics set our system apart from traditional ecological data collecting and processing methods. First, the continuous video recording results in enormous data volumes of monitoring data. Currently around a year of video recordings (containing over the 4 million fish observations) have been processed. Second, different from traditional manual recording and analysing the ecological data, the whole recording, analysing and presentation of results is automated in this system. On one hand, it saves the effort of manually examining every video, which is infeasible. On the other hand, no automatic video analysis method is perfect, so the user interface provides marine ecologists with multiple options to verify the data. Marine ecologists can examine the underlying videos, check results of automatic video analysis at different certainty levels computed by our system, and compare results generated by multiple versions of automatic video analysis software to verify the data in our system. This research tool enables marine ecologists for the first time to analyse long-term and continuous underwater video records.

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Section: Discussionmentioning

confidence: 99%

“…Plankton is much smaller than fish, so specialised sensing equipment is necessary. Software has been developed to classify up to 10-20 taxonomic classes with an accuracy of around 70-80% [4]. Examples of software for plankton classification are Visual Plankton [8], PICES [23] and ZOOSCAN 4 .…”

Section: Diver Observations Video Recordingmentioning

confidence: 99%

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Boom

Palazzo

et al. 2014

Ecological Informatics

121

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“…Systems such as the In Situ Ichthyoplankton Imaging System ISIIS (Cowen and Guigand, 2008), the Zooplankton Visualization and Imaging System ZOOVIS (Benfield et al, 2007) and the Shadowed Image Particle Profiler and Evaluation Recorder (SIP-PER, Samson et al, 2001) allow counting and sizing of large zooplankton from images. The Video Plankton Recorder (VPR, Davis et al, 2005) and the Underwater Vision Profiler (UVP, Picheral et al, 2010) provide high temporal resolution particle imaging and size individual particles >$100 lm.…”

Section: Optical Imaging Of Particle Abundance and Size Distributionmentioning

confidence: 99%

Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES

Boss

Guidi

Richardson

et al. 2015

Progress in Oceanography

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a b s t r a c tField and laboratory characterization of marine particles is laborious and expensive. Proxies of particle properties have been developed that allow researchers to obtain high frequency distributions of such properties in space or time. We focus on optical techniques used to characterize marine particles in-situ, with a focus on GEOTRACES-relevant properties, such as bulk properties including particle mass, crosssectional area, particle size distribution, particle shape information, and also single particle optical properties, such as individual particle type and size. We also address the use of optical properties of particles to infer particulate organic or inorganic carbon. In addition to optical sensors we review advances in imaging technology and its use to study marine particles in situ. This review addresses commercially available technology and techniques that can be used as a proxy for particle properties and the associated uncertainties with particular focus to open ocean environments, the focus of GEOTRACES.

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“…For example, when combined with underwater electronics, 3-D RI imaging of live phytoplankton could be of particular interest to marine microbiologists trying to study biological oceanography [67] and plankton ecology [68]. In addition, 3-D RI tomogram of individual phytoplankton can also be utilized for automated plankton identification [69] or the study of fluid dynamics of plankton [62,70,71]. …”

Section: Discussionmentioning

confidence: 99%

High-Resolution 3-D Refractive Index Tomography and 2-D Synthetic Aperture Imaging of Live Phytoplankton

Lee¹,

K²,

Mubarok³

et al. 2014

Journal of the Optical Society of Korea

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Optical measurements of the morphological and biochemical imaging of phytoplankton are presented. Employing quantitative phase imaging techniques, 3-D refractive index maps and high-resolution 2-D quantitative phase images of individual live phytoplankton are simultaneously obtained without exogenous labeling agents. In addition, biochemical information of individual phytoplankton including volume, mass, and density of individual phytoplankton are also quantitatively obtained from the measured refractive index distributions. We expect the present method to become a powerful tool for the study of phytoplankton.

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RAPID: Research on Automated Plankton Identification

Cited by 238 publications

References 31 publications

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage

Optical techniques for remote and in-situ characterization of particles pertinent to GEOTRACES

High-Resolution 3-D Refractive Index Tomography and 2-D Synthetic Aperture Imaging of Live Phytoplankton

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