Show simple item record

dc.contributor.authorRavanbakhsh, M.
dc.contributor.authorShortis, M.
dc.contributor.authorShafait, F.
dc.contributor.authorMian, A.
dc.contributor.authorHarvey, Euan
dc.contributor.authorSeager, J.
dc.identifier.citationRavanbakhsh, M. and Shortis, M. and Shafait, F. and Mian, A. and Harvey, E. and Seager, J. 2015. Automated Fish Detection in Underwater Images Using Shape-Based level Sets. The Photogrammetric Record. 30 (149): pp. 46-62.

Underwater stereo-video systems are widely used for the measurement of fish. However, the effectiveness of stereo-video measurement has been limited because most operational systems still rely on a human operator. In this paper an automated approach for fish detection, using a shape-based level-sets framework, is presented. Knowledge of the shape of fish is modelled by principal component analysis (PCA). The Haar classifier is used for precise localisation of the fish head and snout in the image, which is vital information for close-proximity initialisation of the shape model. The approach has been tested on underwater images representing a variety of challenging situations typical of the underwater environment, such as background interference and poor contrast boundaries. The results obtained demonstrate that the approach is capable of overcoming these difficulties and capturing the fish outline to sub-pixel accuracy.

dc.publisherWiley-Blackwell Publishing
dc.subjectfish detection
dc.subjectunderwater image
dc.subjectprior shape knowledge
dc.subjectlevel sets
dc.subjectimage segmentation
dc.titleAutomated Fish Detection in Underwater Images Using Shape-Based level Sets
dc.typeJournal Article
dcterms.source.titleThe Photogrammetric Record: an international journal of photogrammetry
curtin.departmentDepartment of Environment and Agriculture
curtin.accessStatusFulltext not available

Files in this item


This item appears in the following Collection(s)

Show simple item record