Image Segmentation from Shadow-Hints using Minimum Spanning Trees
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913575560282112 |
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| author | Heep, Moritz Zell, Eduard |
| author_facet | Heep, Moritz Zell, Eduard |
| contents | Image segmentation in RGB space is a notoriously difficult task where state-of-the-art methods are trained on thousands or even millions of annotated images. While the performance is impressive, it is still not perfect. We propose a novel image segmentation method, achieving similar segmentation quality but without training. Instead, we require an image sequence with a static camera and a single light source at varying positions, as used in for photometric stereo, for example. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Image Segmentation from Shadow-Hints using Minimum Spanning Trees Heep, Moritz Zell, Eduard Computer Vision and Pattern Recognition Graphics Image segmentation in RGB space is a notoriously difficult task where state-of-the-art methods are trained on thousands or even millions of annotated images. While the performance is impressive, it is still not perfect. We propose a novel image segmentation method, achieving similar segmentation quality but without training. Instead, we require an image sequence with a static camera and a single light source at varying positions, as used in for photometric stereo, for example. |
| title | Image Segmentation from Shadow-Hints using Minimum Spanning Trees |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2411.06530 |