Underwater video is increasingly being pursued as a low impact alternative to traditional techniques (such as trawls and dredges) for determining abundance and size frequency of target species. Our research focuses on automatically annotating survey scallop video footage using artificial intelligence techniques. We use a multi-layered approach which implements an attention selection process followed by sub-image segmentation and classification. Initial attention selection is performed using the University of Southern California's (USCs) iLab Neuromorphic Visual Toolkit (iNVT). Once the iNVT has determined regions of potential interest we use image segmentation and feature extraction techniques to produce data suitable for analysis within the Weka machine learning workbench environment. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Fearn, R., Williams, R., Cameron-Jones, M., Harrington, J., & Semmens, J. (2007). Automated intelligent abundance analysis of scallop survey video footage. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4830 LNAI, pp. 549–558). Springer Verlag. https://doi.org/10.1007/978-3-540-76928-6_56
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