Study on Image Background Removal using Deep Learning
Removing image backgrounds is a common job in image processing and computer vision. By isolating the main object from the back, background removal in photographs aims to make it easier to examine or edit the image. There are numerous methods for removing the background from an image, including...
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1921/1/jods2024_06.pdf http://eprints.intimal.edu.my/1921/ http://ipublishing.intimal.edu.my/jods.html |
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| _version_ | 1831337532481077248 |
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| author | Fakruddin Ali, Ahmed Harprith Kaur, Rajinder Singh |
| author_facet | Fakruddin Ali, Ahmed Harprith Kaur, Rajinder Singh |
| author_sort | Fakruddin Ali, Ahmed |
| building | INTI Library |
| collection | Institutional Repository |
| content_provider | INTI International University |
| content_source | INTI Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Removing image backgrounds is a common job in image processing and computer vision. By
isolating the main object from the back, background removal in photographs aims to make it easier
to examine or edit the image. There are numerous methods for removing the background from an
image, including deep learning, color-based segmentation, and human selection. The U-Net
architecture, one of the deep learning-based techniques, has demonstrated encouraging results in
image segmentation tasks, including image background removal. A convolutional neural network
created for biological image segmentation is known as the U-Net architecture. The design consists
of an encoder network that stores the context and a decoder network that generates the
segmentation map. The U-shape of the U-Net architecture enables it to record both the overall
context and the local specifics of the image. For several picture segmentation tasks, including
image background removal, U-Net architecture has undergone modification. The suggested
method for removing image backgrounds using U-Net entails training a U-Net model on a dataset
of pictures with and without background. Then, using the demonstrated methodology, the
backdrop is removed from recent photographs. The suggested method differs from current
approaches in various, including its high accuracy and capacity to handle complicated backgrounds.
Computer vision, object identification, and photo manipulation are just a few of the uses for the
suggested method |
| format | Article |
| id | my-inti-eprints.1921 |
| institution | INTI International University |
| language | en |
| publishDate | 2024 |
| publisher | INTI International University |
| record_format | eprints |
| spelling | my-inti-eprints.19212024-06-04T06:39:07Z http://eprints.intimal.edu.my/1921/ Study on Image Background Removal using Deep Learning Fakruddin Ali, Ahmed Harprith Kaur, Rajinder Singh Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Removing image backgrounds is a common job in image processing and computer vision. By isolating the main object from the back, background removal in photographs aims to make it easier to examine or edit the image. There are numerous methods for removing the background from an image, including deep learning, color-based segmentation, and human selection. The U-Net architecture, one of the deep learning-based techniques, has demonstrated encouraging results in image segmentation tasks, including image background removal. A convolutional neural network created for biological image segmentation is known as the U-Net architecture. The design consists of an encoder network that stores the context and a decoder network that generates the segmentation map. The U-shape of the U-Net architecture enables it to record both the overall context and the local specifics of the image. For several picture segmentation tasks, including image background removal, U-Net architecture has undergone modification. The suggested method for removing image backgrounds using U-Net entails training a U-Net model on a dataset of pictures with and without background. Then, using the demonstrated methodology, the backdrop is removed from recent photographs. The suggested method differs from current approaches in various, including its high accuracy and capacity to handle complicated backgrounds. Computer vision, object identification, and photo manipulation are just a few of the uses for the suggested method INTI International University 2024-05-29 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1921/1/jods2024_06.pdf Fakruddin Ali, Ahmed and Harprith Kaur, Rajinder Singh (2024) Study on Image Background Removal using Deep Learning. Journal of Data Science, 2024 (06). pp. 1-6. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Fakruddin Ali, Ahmed Harprith Kaur, Rajinder Singh Study on Image Background Removal using Deep Learning |
| title | Study on Image Background Removal using Deep Learning |
| title_full | Study on Image Background Removal using Deep Learning |
| title_fullStr | Study on Image Background Removal using Deep Learning |
| title_full_unstemmed | Study on Image Background Removal using Deep Learning |
| title_short | Study on Image Background Removal using Deep Learning |
| title_sort | study on image background removal using deep learning |
| topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1921/1/jods2024_06.pdf http://eprints.intimal.edu.my/1921/ http://ipublishing.intimal.edu.my/jods.html |
| url_provider | http://eprints.intimal.edu.my |