| Autor: |
Chakrabarti, Ayan, Xiong, Ying, Sun, Baochen, Darrell, Trevor, Scharstein, Daniel, Zickler, Todd, Saenko, Kate |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
IEEE Trans. PAMI 36 (2014) 2185-2198 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TPAMI.2014.2318713 |
| Popis: |
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range. This introduces non-linear distortion that must be undone, through a radiometric calibration process, before computer vision systems can analyze such photographs radiometrically. This paper considers the inherent uncertainty of undoing the effects of tone-mapping. We observe that this uncertainty varies substantially across color space, making some pixels more reliable than others. We introduce a model for this uncertainty and a method for fitting it to a given camera or imaging pipeline. Once fit, the model provides for each pixel in a tone-mapped digital photograph a probability distribution over linear scene colors that could have induced it. We demonstrate how these distributions can be useful for visual inference by incorporating them into estimation algorithms for a representative set of vision tasks. |
| Databáze: |
arXiv |
| Externí odkaz: |
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