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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Nanyang Technological University
2024
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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 |
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Computer and Information Science Lightweight segmentation Computer vision Yeo, Tzun Kai Lightweight image segmentation |
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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. |
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Deepu Rajan |
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Deepu Rajan Yeo, Tzun Kai |
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Final Year Project |
author |
Yeo, Tzun Kai |
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Yeo, Tzun Kai |
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Lightweight image segmentation |
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Lightweight image segmentation |
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Lightweight image segmentation |
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Lightweight image segmentation |
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Lightweight image segmentation |
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lightweight image segmentation |
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Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/175006 |
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1800916179872645120 |