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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2312.17010 |
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| _version_ | 1866912006835011584 |
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| author | Dutta, Aritra Suseela, G Sood, Asmita |
| author_facet | Dutta, Aritra Suseela, G Sood, Asmita |
| contents | Multi-modal image stitching can be a difficult feat. That's why, in this paper, we've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality - irrespective of lighting, scale or orientation differences between images. We've put our pipeline to the test with a varied dataset and found that it's very effective in enhancing scene understanding and finding real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17010 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust Multi-Modal Image Stitching for Improved Scene Understanding Dutta, Aritra Suseela, G Sood, Asmita Computer Vision and Pattern Recognition Multi-modal image stitching can be a difficult feat. That's why, in this paper, we've devised a unique and comprehensive image-stitching pipeline that taps into OpenCV's stitching module. Our approach integrates feature-based matching, transformation estimation, and blending techniques to bring about panoramic views that are of top-tier quality - irrespective of lighting, scale or orientation differences between images. We've put our pipeline to the test with a varied dataset and found that it's very effective in enhancing scene understanding and finding real-world applications. |
| title | Robust Multi-Modal Image Stitching for Improved Scene Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.17010 |