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dc.contributor.authorAnsari, S.
dc.date.accessioned2019/04/29
dc.date.accessioned2019-04-30T12:48:23Z
dc.date.available2019/02/22
dc.date.available2019-04-30T12:48:23Z
dc.date.issued2019
dc.identifier.citationIn:Proceedings of the 2019 Third IEEE International Conference on Electrical,Computer and Communication Technologies (IEEE ICECCT 2019), 20-22 February, Coimbatore, India,. IEEE, Coimbatore, pp. 972-974
dc.identifier.isbn978-1-5386-8157-2
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/8098
dc.description.abstractFast and robust image recognition and matching is an important task in the underwater domain. Autonomous Underwater Vehicles (AUV) use images for detecting and recognising object or scene of interest. Interest points are extracted from images that are captured by the camera on the AUV. In this paper, feature detectors SIFT and SURF are investigated as applicable to the underwater environment. They are reviewed and compared in terms of robustness, accuracy, and speed with the aim of identifying a method which is computationally-efficient in finding good corresponding points between two underwater images
dc.languageen
dc.publisherInstitute of Electrical and Electronic Engineers, Inc. USA
dc.relation.isreferencedbyNON-SCI
dc.titleA review on SIFT and SURF for underwater image feature detection and matching
dc.typeConference Article
dc.affiliation.authorCSIR-National Institute of Oceanography, Dona Paula, Goa, India


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