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dc.contributor.authorSachdeva, Parmeet Singh
dc.date.accessioned2019-08-09T17:40:57Z
dc.date.available2019-08-09T17:40:57Z
dc.identifier.urihttp://hdl.handle.net/10222/76243
dc.description.abstractThe thesis explores the efficacy of convolutional neural network(CNNs) to categorize lobster images for improving lobster grading and traceability. Traceability ensures that lobsters are traceable to a sustainable source. Lobsters kept in unsuitable conditions such as extremely low temperatures or densely packed crates have low chances of survival leading to a lower grade ultimately affecting prices. The CNNs were able to achieve high accuracies for assessment of lobster traits. Attention mechanisms that learn to extract discriminating features were explored to improve the performance of CNNs. The attention augmented CNNs had similar accuracies compared to the vanilla CNN but were less sensitive to choice of architecture and learning rate. The attention CNNs could map landmarks on lobster images(for sizing) with an acceptable error of about 2cm. Additionally, siamese networks, that were explored for a black box approach towards uniquely identifying lobsters, were able to achieve a top-3 accuracy of about 84%.en_US
dc.language.isoenen_US
dc.subjectConvolution neural networksen_US
dc.subjectMachine learningen_US
dc.subjectMarine animalsen_US
dc.subjectlobstersen_US
dc.titleANALYSING MARINE ANIMALS CHARACTERISTICS USING CONVOLUTIONAL NEURAL NETWORKSen_US
dc.date.defence2019-08-01
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorMichael McAllisteren_US
dc.contributor.thesis-readerSrinivas Sampallien_US
dc.contributor.thesis-readerMalcolm Heywooden_US
dc.contributor.thesis-supervisorMae Setoen_US
dc.contributor.thesis-supervisorThomas Trappenburgen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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