MEPS 312:297-309 (2006)  -  doi:10.3354/meps312297

Automatic image analysis of plankton: future perspectives

Phil F. Culverhouse1,**, Robert Williams2, Mark Benfield3, Per R. Flood4, Anne F. Sell5, Maria Grazia Mazzocchi6, Isabella Buttino6, Mike Sieracki7

1Centre for Interactive Intelligent Systems, University of Plymouth, Plymouth PL4 8AA, UK
2Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
3Louisiana State University, Coastal Fisheries Institute, Dept. Oceanology and Coastal Sciences, Baton Rouge, Lousiana 70803, USA
4Bathybiologica A/S, Gerhard Grans vei 58, 5081 Bergen, Norway
5Institute for Hydrobiology and Fisheries Science, University of Hamburg, Olbersweg 24, 22767 Hamburg, Germany
6Stazione Zoologica ‘A Dohrn’, Villa Comunale, 80121 Naples, Italy
7J. J. MacIsaac Facility for Individual Particle Analysis, Bigelow Laboratory for Ocean Sciences, 180 McKown Point, PO Box 475, West Boothbay Harbor, Maine 04575-0475, USA

ABSTRACT: In the future, if marine science is to achieve any progress in addressing biological diversity of ocean plankton, then it needs to sponsor development of new technology. One requirement is the development of high-resolution sensors for imaging field-collected and in situ specimens in a non-invasive manner. The rapid automatic categorisation of species must be accompanied by the creation of very large distributed databases in the form of high-resolution 3D rotatable images of species, which could become the standard reference source for automatic identification. These 3D images will serve as classification standards for field applications, and (in adjusted optical quality) as training templates for image analysis systems based on statistical and other pattern-matching processes. This paper sets out the basic argument for such developments and proposes a long-term solution to achieve these aims.


KEY WORDS: Natural object recognition · Object categorization · Zooplankton · Phytoplankton · Imaging · Taxonomy · Automatic identification · Image analysis


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