Lightweight image segmentation

Deploying advanced image segmentation tasks on mobile devices struggle with the demands of sophisticated deep learning models. Image segmentation, a critical task in computer vision, has seen remarkable advancements through deep learning. However, the implementation of these computationally inten...

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Main Author: Yeo, Tzun Kai
Other Authors: Deepu Rajan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175006
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1750062024-04-19T15:45:16Z Lightweight image segmentation Yeo, Tzun Kai Deepu Rajan School of Computer Science and Engineering ASDRajan@ntu.edu.sg Computer and Information Science Lightweight segmentation Computer vision Deploying advanced image segmentation tasks on mobile devices struggle with the demands of sophisticated deep learning models. Image segmentation, a critical task in computer vision, has seen remarkable advancements through deep learning. However, the implementation of these computationally intensive models on mobile devices is hindered by their large size and resource demands. The project aims to develop a mobile-friendly, lightweight deep learning architecture for image segmentation, drawing inspiration from DeepLabV3’s capabilities. The goal is to balance the trade-off between accuracy and speed, thereby making advanced image segmentation feasible on mobile platforms. Bachelor's degree 2024-04-18T06:15:49Z 2024-04-18T06:15:49Z 2024 Final Year Project (FYP) Yeo, T. K. (2024). Lightweight image segmentation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175006 https://hdl.handle.net/10356/175006 en SCSE23-0503 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Lightweight segmentation
Computer vision
spellingShingle Computer and Information Science
Lightweight segmentation
Computer vision
Yeo, Tzun Kai
Lightweight image segmentation
description Deploying advanced image segmentation tasks on mobile devices struggle with the demands of sophisticated deep learning models. Image segmentation, a critical task in computer vision, has seen remarkable advancements through deep learning. However, the implementation of these computationally intensive models on mobile devices is hindered by their large size and resource demands. The project aims to develop a mobile-friendly, lightweight deep learning architecture for image segmentation, drawing inspiration from DeepLabV3’s capabilities. The goal is to balance the trade-off between accuracy and speed, thereby making advanced image segmentation feasible on mobile platforms.
author2 Deepu Rajan
author_facet Deepu Rajan
Yeo, Tzun Kai
format Final Year Project
author Yeo, Tzun Kai
author_sort Yeo, Tzun Kai
title Lightweight image segmentation
title_short Lightweight image segmentation
title_full Lightweight image segmentation
title_fullStr Lightweight image segmentation
title_full_unstemmed Lightweight image segmentation
title_sort lightweight image segmentation
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175006
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