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Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues

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dc.contributor.author Mehrnejad, Marzieh
dc.date.accessioned 2015-08-13T20:21:48Z
dc.date.available 2015-08-13T20:21:48Z
dc.date.copyright 2015 en_US
dc.date.issued 2015-08-13
dc.identifier.uri http://hdl.handle.net/1828/6439
dc.description.abstract Underwater video data are a rich source of information for marine biologists. However, the large amount of recorded video creates a ’big data’ problem, which emphasizes the need for automated detection techniques. This work focuses on the detection of quasi-stationary crabs of various sizes in deep-sea images. Specific issues related to image quality such as low contrast and non-uniform lighting are addressed by the pre-processing step. The segmentation step is based on color, size and shape considerations. Segmentation identifies regions that potentially correspond to crabs. These regions are normalized to be invariant to scale and translation. Feature vectors are formed by the normalized regions, and they are further classified via supervised and non-supervised machine learning techniques. The proposed approach is evaluated experimentally using a video dataset available from Ocean Networks Canada. The thesis provides an in-depth discussion about the performance of the proposed algorithms. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/ *
dc.subject Computer Vision en_US
dc.subject Classification en_US
dc.subject Neural Networks en_US
dc.subject Machine Learning en_US
dc.title Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues en_US
dc.type Thesis en_US
dc.contributor.supervisor Branzan Albu, Alexandra
dc.contributor.supervisor Capson, David
dc.degree.department Department of Electrical and Computer Engineering en_US
dc.degree.level Master of Applied Science M.A.Sc. en_US
dc.identifier.bibliographicCitation Mehrnejad, Marzieh, et al. "Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues." Computer Vision for Analysis of Underwater Imagery (CVAUI), 2014 ICPR Workshop on. IEEE, 2014. en_US
dc.identifier.bibliographicCitation Mehrnejad, M., Branzan Albu, A., Capson, D., Hoeberechts, M.: Detection of stationary animals in deep-sea video. In: Oceans - San Diego, 2013. (Sept 2013) 1-5 en_US
dc.description.scholarlevel Graduate en_US
dc.description.proquestcode 0544 en_US
dc.description.proquestcode 0800 en_US
dc.description.proquestcode 0547 en_US


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