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

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dc.contributor.authorMehrnejad, Marzieh
dc.contributor.supervisorBranzan Albu, Alexandra
dc.contributor.supervisorCapson, David
dc.date.accessioned2015-08-13T20:21:48Z
dc.date.available2015-08-13T20:21:48Z
dc.date.copyright2015en_US
dc.date.issued2015-08-13
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractUnderwater 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.description.proquestcode0544en_US
dc.description.proquestcode0800en_US
dc.description.proquestcode0547en_US
dc.description.proquestemailmars_mehr@hotmail.comen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationMehrnejad, 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.bibliographicCitationMehrnejad, M., Branzan Albu, A., Capson, D., Hoeberechts, M.: Detection of stationary animals in deep-sea video. In: Oceans - San Diego, 2013. (Sept 2013) 1-5en_US
dc.identifier.urihttp://hdl.handle.net/1828/6439
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectComputer Visionen_US
dc.subjectClassificationen_US
dc.subjectNeural Networksen_US
dc.subjectMachine Learningen_US
dc.titleTowards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cuesen_US
dc.typeThesisen_US

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