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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Fearn, RC and Williams, RN and Cameron-Jones, RM and Harrington, JJ and Semmens, JM
Keywords:	Scallop Survey Video Transects, Automated Video Annotation.
Additional Information:	Author version pre-print. Subsequently published in AI 2007: Advances in Artificial Intelligence 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings Series: Lecture Notes in Computer Science Subseries: Lecture Notes in Artificial Intelligence , Vol. 4830 ISBN: 978-3-540-76926-2
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