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Authors: Piyanuch Silapachote 1 ; Frank R. Stolle 1 ; Allen R. Hanson 1 and Cynthia H. Pilskaln 2

Affiliations: 1 University of Massachusetts Amherst, United States ; 2 University of Massachusetts Dartmouth, United States

ISBN: 978-989-674-029-0

ISSN: 2184-4321

Keyword(s): Marine science application, Biologically inspired vision system, Image segmentation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segmentation and Grouping ; Sensor Networks ; Soft Computing

Abstract: Covering over 70% of the Earth’s surface and containing over 95% of the planet’s water, the aquatic ecosystem has a great influence on many environmental functions. An indicator of the health of a marine habitat is its populations, estimated by taking underwater images and labeling various species. Designing an automated algorithm for this task is quite a challenge. Image quality tends to be low due to the dynamics of the water body. The diversity of shapes and motions among living plankton and non-living detritus are remarkable. We have applied two very different techniques from computer vision to the automatic labeling of tiny planktonic organisms. One is a common approach involving segmentation and calculations of statistical features. The other is inspired by the sophisticated visual processing in primates. Both achieved competitively high accuracies, comparable to general agreement among expert marine scientists. We found that a relatively simple biologically motivated system can be as effective as a more complicated classical schema in this domain. (More)

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Paper citation in several formats:
Silapachote P.; R. Stolle F.; R. Hanson A.; Pilskaln C. and (2010). CLASSIFICATION OF CHALLENGING MARINE IMAGERY.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 401-406. DOI: 10.5220/0002835304010406

@conference{visapp10,
author={Piyanuch Silapachote and Frank {R. Stolle} and Allen {R. Hanson} and Cynthia H. Pilskaln},
title={CLASSIFICATION OF CHALLENGING MARINE IMAGERY},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={401-406},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002835304010406},
isbn={978-989-674-029-0},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - CLASSIFICATION OF CHALLENGING MARINE IMAGERY
SN - 978-989-674-029-0
AU - Silapachote, P.
AU - R. Stolle, F.
AU - R. Hanson, A.
AU - Pilskaln, C.
PY - 2010
SP - 401
EP - 406
DO - 10.5220/0002835304010406

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