- author = "Lorenzo Corgnati and Luca Mazzei and Simone Marini and Stefano Aliani and Alessandra Conversi and Annalisa Griffa and Bruno Isoppo and Ennio Ottaviani",
- booktitle = "ICPR Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI 2014)",
- title = "Automated Gelatinous Zooplankton Acquisition and Recognition",
- year = "2014",
- month = "24-24 " # aug,
- address = "Stockholm",
- keywords = "genetic algorithms, genetic programming, SVM, pattern recognition, gelatinous zooplankton, underwater imaging, feature selection, underwater camera, GUARD1, autonomous vehicle",
- isbn13 = "978-1-4799-6713-1",
-
DOI = "
doi:10.1109/CVAUI.2014.12",
- size = "8 pages",
- abstract = "Much is still unknown about marine plankton abundance and dynamics in the open and interior ocean. Especially challenging is the knowledge of gelatinous zooplankton distribution, since it has a very fragile structure and cannot be directly sampled using traditional net based techniques. In the last decades there has been an increasing interest in the oceanographic community toward imaging systems. In this paper the performance of three different methodologies, Tikhonov regularisation, Support Vector Machines, and Genetic Programming, are analysed for the recognition of gelatinous zooplankton. The three methods have been tested on images acquired in the Ligurian Sea by a low cost under-water standalone system (GUARD1). The results indicate that the three methods provide gelatinous zooplankton identification with high accuracy showing a good capability in robustly selecting relevant features, thus avoiding computational-consuming preprocessing stages. These aspects fit the requirements for running on an autonomous imaging system designed for long lasting deployments.",
-
notes = "Also known as \cite{6961262}
ISMAR-Marine Sciences Institute in La Spezia, CNR-National Research Council of Italy, Lerici, SP, Italy",
