CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908576119783424 |
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| author | Wong, Yuk-Kwan Zheng, Ziqiang Zhang, Mingzhe Suggett, David Yeung, Sai-Kit |
| author_facet | Wong, Yuk-Kwan Zheng, Ziqiang Zhang, Mingzhe Suggett, David Yeung, Sai-Kit |
| contents | Coral reef imagery offers critical data for monitoring ecosystem health, in particular as the ease of image datasets continues to rapidly expand. Whilst semi-automated analytical platforms for reef imagery are becoming more available, the dominant approaches face fundamental limitations. To address these challenges, we propose CoralSCOP-LAT, a coral reef image analysis and labeling tool that automatically segments and analyzes coral regions. By leveraging advanced machine learning models tailored for coral reef segmentation, CoralSCOP-LAT enables users to generate dense segmentation masks with minimal manual effort, significantly enhancing both the labeling efficiency and precision of coral reef analysis. Our extensive evaluations demonstrate that CoralSCOP-LAT surpasses existing coral reef analysis tools in terms of time efficiency, accuracy, precision, and flexibility. CoralSCOP-LAT, therefore, not only accelerates the coral reef annotation process but also assists users in obtaining high-quality coral reef segmentation and analysis outcomes. Github Page: https://github.com/ykwongaq/CoralSCOP-LAT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask Wong, Yuk-Kwan Zheng, Ziqiang Zhang, Mingzhe Suggett, David Yeung, Sai-Kit Computer Vision and Pattern Recognition Coral reef imagery offers critical data for monitoring ecosystem health, in particular as the ease of image datasets continues to rapidly expand. Whilst semi-automated analytical platforms for reef imagery are becoming more available, the dominant approaches face fundamental limitations. To address these challenges, we propose CoralSCOP-LAT, a coral reef image analysis and labeling tool that automatically segments and analyzes coral regions. By leveraging advanced machine learning models tailored for coral reef segmentation, CoralSCOP-LAT enables users to generate dense segmentation masks with minimal manual effort, significantly enhancing both the labeling efficiency and precision of coral reef analysis. Our extensive evaluations demonstrate that CoralSCOP-LAT surpasses existing coral reef analysis tools in terms of time efficiency, accuracy, precision, and flexibility. CoralSCOP-LAT, therefore, not only accelerates the coral reef annotation process but also assists users in obtaining high-quality coral reef segmentation and analysis outcomes. Github Page: https://github.com/ykwongaq/CoralSCOP-LAT. |
| title | CoralSCOP-LAT: Labeling and Analyzing Tool for Coral Reef Images with Dense Mask |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.20436 |