Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162227
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dc.contributor.authorTang, Alvin Kai Wenen_US
dc.date.accessioned2022-10-10T08:20:20Z-
dc.date.available2022-10-10T08:20:20Z-
dc.date.issued2022-
dc.identifier.citationTang, A. K. W. (2022). Capitalizing deep neural network with multifaceted semantic image segmentation integration methodology. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162227en_US
dc.identifier.urihttps://hdl.handle.net/10356/162227-
dc.description.abstractThe domain of unsupervised adaptation has always posed an intricate problem for the field of semantic image segmentation. The lack of information to predict each pixel has led to current research implementing the novelty of deep neural network applications to handle this issue. However, these methodologies are usually unimodal which, with proper integration strategies, form a deep multifaceted methodology that could achieve a better result. Thus, this paper has presented various unimodal along with conventional segmentation techniques that do not utilize the deep neural network. After which, the main methodology investigated possible integration techniques which encompassed early, late, and hybrid integration. A structured framework formulated from relevant datasets and performance benchmarks has been utilized to properly evaluate the results obtained. Limitations faced and a comprehensive evaluation of integration methodologies were discussed afterward to provide holistic insights as to when and how to utilize this integration methodology.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0656en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleCapitalizing deep neural network with multifaceted semantic image segmentation integration methodologyen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLu Shijianen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailShijian.Lu@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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