Webcam clip art: appearance and illuminant transfer from time-lapse sequences
ACM SIGGRAPH Asia 2009 papers
Removing image artifacts due to dirty camera lenses and thin occluders
ACM SIGGRAPH Asia 2009 papers
What Do the Sun and the Sky Tell Us About the Camera?
International Journal of Computer Vision
Nonuniform lattice regression for modeling the camera imaging pipeline
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Dating historical color images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
How bright is the moon? recovering and using absolute luminance values from internet images
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Camera Spectral Sensitivity and White Balance Estimation from Sky Images
International Journal of Computer Vision
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A large photo collection downloaded from the internet spans a wide range of scenes, cameras, and photographers. In this paper we introduce several novel priors for statistics of such large photo collections that are independent of these factors. We then propose that properties of these factors can be recovered by examining the deviation between these statistical priors and the statistics of a slice of the overall photo collection that holds one factor constant. Specifically, we recover the radiometric properties of a particular camera model by collecting numerous images captured by it, and examining the deviation of this collection's statistics from that of a broader photo collection whose camera-specific effects have been removed. We show that using this approach we can recover both a camera model's non-linear response function and the spatially-varying vignetting of the camera's different lens settings. All this is achieved using publicly available photographs, without requiring images captured under controlled conditions or physical access to the cameras. We also apply this concept to identify bad pixels on the detectors of specific camera instances. We conclude with a discussion of future applications of this general approach to other common computer vision problems.