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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11063 |
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| _version_ | 1866915243788075008 |
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| author | Diaz-Garcia, Pedro Escalona, Felix Cazorla, Miguel |
| author_facet | Diaz-Garcia, Pedro Escalona, Felix Cazorla, Miguel |
| contents | The purpose of this paper is to explore the use of underwater image enhancement techniques to improve keypoint detection and matching. By applying advanced deep learning models, including generative adversarial networks and convolutional neural networks, we aim to find the best method which improves the accuracy of keypoint detection and the robustness of matching algorithms. We evaluate the performance of these techniques on various underwater datasets, demonstrating significant improvements over traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques Diaz-Garcia, Pedro Escalona, Felix Cazorla, Miguel Computer Vision and Pattern Recognition The purpose of this paper is to explore the use of underwater image enhancement techniques to improve keypoint detection and matching. By applying advanced deep learning models, including generative adversarial networks and convolutional neural networks, we aim to find the best method which improves the accuracy of keypoint detection and the robustness of matching algorithms. We evaluate the performance of these techniques on various underwater datasets, demonstrating significant improvements over traditional methods. |
| title | UKDM: Underwater keypoint detection and matching using underwater image enhancement techniques |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.11063 |