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Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
ISSN: 0162-8828
Ayan Chakrabarti, AC is with the Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138. (E-mail: ayanc@eecs.harvard.edu).
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.
Ying Xiong, Daniel Scharstein, Ayan Chakrabarti, Trevor Darrell, Baochen Sun, Kate Saenko, Todd Zickler, "Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 April 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2014.2318713>
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