ANALYZING STYLE TRANSFER ALGORITHMS FOR SEGMENTED IMAGES
dc.contributor.advisor | Dr. David Hart | |
dc.contributor.author | Seyed, Seyedhadi | |
dc.contributor.committeeMember | Dr. Rui Wu | |
dc.contributor.committeeMember | Dr. Nic Herndon | |
dc.contributor.department | Computer Science | |
dc.date.accessioned | 2025-01-28T17:00:54Z | |
dc.date.available | 2025-01-28T17:00:54Z | |
dc.date.created | 2024-12 | |
dc.date.issued | December 2024 | |
dc.date.submitted | December 2024 | |
dc.date.updated | 2025-01-26T14:14:49Z | |
dc.degree.college | College of Engineering and Technology | |
dc.degree.grantor | East Carolina University | |
dc.degree.major | MS-Data Science | |
dc.degree.name | M.S. | |
dc.degree.program | MS-Data Science | |
dc.description.abstract | The recently developed Segment Anything Model has made grabbing semantically meaningful regions of an image easier than before. This will allow for new applications that build on this approach that weren’t previously possible. This thesis investigates integrating the Segment Anything Model with style transfer. Specifically, it proposes Partial Convolution as a way to improve style transfer for segmented regions. Additionally, it investigates how different style transfer techniques are affected by different mask sizes, image statistics, etc. | |
dc.etdauthor.orcid | 0009-0004-3655-8732 | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/10342/13838 | |
dc.language.iso | English | |
dc.publisher | East Carolina University | |
dc.subject | Computer Science | |
dc.title | ANALYZING STYLE TRANSFER ALGORITHMS FOR SEGMENTED IMAGES | |
dc.type | Master's Thesis | |
dc.type.material | text |
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