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Identifying sea scallops from benthic camera images
Author(s) -
Kannappan Prasanna,
Walker Justin H.,
Trembanis Art,
Tanner Herbert G.
Publication year - 2014
Publication title -
limnology and oceanography: methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.898
H-Index - 72
ISSN - 1541-5856
DOI - 10.4319/lom.2014.12.680
Subject(s) - artificial intelligence , computer science , segmentation , computer vision , underwater , matching (statistics) , template matching , image segmentation , process (computing) , population , modular design , graph , pattern recognition (psychology) , geology , image (mathematics) , mathematics , oceanography , sociology , operating system , theoretical computer science , statistics , demography
The article presents an algorithmic framework for the automated analysis of benthic imagery data. The data are collected by an autonomous underwater vehicle for the purpose of population assessment of epibenthic organisms, such as scallops. The architecture consists of three layers of processing: visual attention, graph‐cut segmentation methods, and template matching. The visual attention layer filters the imagery input, focusing subsequent processing only on regions in the images that are likely to contain target objects. The segmentation layer prepares for subsequent template matching. Finally, template matching classifies filtered objects into targets and distractors. The significance of the proposed approach is in its modular nature and its ability to process imagery datasets of low resolution, brightness, and contrast.