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dc.contributor.authorCorrigan, D.
dc.contributor.authorSooknanan, K.
dc.contributor.authorDoyle, J.
dc.contributor.authorLordan, C.
dc.contributor.authorKokaram, A.
dc.date.accessioned2018-05-09T10:08:07Z
dc.date.available2018-05-09T10:08:07Z
dc.date.issued2018
dc.identifier.citationCorrigan, D., Sooknanan, K., Doyle, J., Lordan, C., & Kokaram, A. (2018). A Low-Complexity Mosaicing Algorithm for Stock Assessment of Seabed-Burrowing Species. IEEE Journal of Oceanic Engineering.en_US
dc.identifier.issn1558-1691
dc.identifier.otherdoi: 10.1109/JOE.2018.2808973
dc.identifier.urihttp://hdl.handle.net/10793/1350
dc.descriptionPeer-reviewed This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. Manuscript received January 27, 2017; revised August 17, 2017 and December 27, 2017; accepted February 16, 2018. Published in: IEEE Journal of Oceanic Engineering ( Early Access ) DOI: 10.1109/JOE.2018.2808973en_US
dc.description.abstractThis paper proposes an algorithm for mosaicing videos generated during stock assessment of seabed-burrowing species. In these surveys, video transects of the seabed are captured and the population is estimated by counting the number of burrows in the video. The mosaicing algorithm is designed to process a large amount of video data and summarize the relevant features for the survey in a single image. Hence, the algorithm is designed to be computationally inexpensive while maintaining a high degree of robustness. We adopt a registration algorithm that employs a simple translational motion model and generates a mapping to the mosaic coordinate system using a concatenation of frame-by-frame homographies. A temporal smoothness prior is used in a maximum a posteriori homography estimation algorithm to reduce noise in the motion parameters in images with small amounts of texture detail. A multiband blending scheme renders the mosaic and is optimized for the application requirements. Tests on a large data set show that the algorithm is robust enough to allow the use of mosaics as a medium for burrow counting. This will increase the verifiability of the stock assessments as well as generate a ground truth data set for the learning of an automated burrow counting algorithm.en_US
dc.description.sponsorshipThis work was supported by the Science Foundation Ireland under Award SFI-PI 08/IN.1/I2112.en_US
dc.language.isoenen_US
dc.publisherIEEE Xploreen_US
dc.relation.ispartofseriesIEEE JOURNAL OF OCEANIC ENGINEERING;
dc.subjectFeature-based image registrationen_US
dc.subjectNephrops Surveysen_US
dc.subjectUnderwater mosaicingen_US
dc.subjectUnderwater television (UWTV)en_US
dc.titleA Low-Complexity Mosaicing Algorithm for Stock Assessment of Seabed-Burrowing Speciesen_US
dc.typeArticleen_US
refterms.dateFOA2018-05-09T10:08:08Z


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