Robot vision
Fundamentals of digital image processing
Fundamentals of digital image processing
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Mosaicing: High Dynamic Range in a Wide Field of View
International Journal of Computer Vision
Determining the Camera Response from Images: What Is Knowable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Space of Camera Response Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Voting for Image Correction by Global and Local Intensity Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera calibration, object tracking and query generation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics)
Video coding for the mobile capture of higher dynamic range image sequences
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Probability models for high dynamic range imaging
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Radiometric calibration from a single image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Camera response functions for image forensics: an automatic algorithm for splicing detection
IEEE Transactions on Information Forensics and Security
Ghost-free high dynamic range imaging
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Gradient domain HDR compositing
ACM SIGGRAPH 2011 Posters
Ghost detection and removal for high dynamic range images: Recent advances
Image Communication
Optimized hierarchical block matching for fast and accurate image registration
Image Communication
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Brightness values of pixels in an image are related to image irradiance by a non-linear function, called the radiometric response function. Recovery of this function is important since many algorithms in computer vision and image processing use image irradiance. Several investigators have described methods for recovery of the radiometric response, without using charts, from multiple exposures of the same scene. All these recovery methods are based solely on the correspondence of gray-levels in one exposure to gray-levels in another exposure. This correspondence can be described by a function we call the brightness transfer function. We show that brightness transfer functions, and thus images themselves, do not uniquely determine the radiometric response function, nor the ratios of exposure between the images. We completely determine the ambiguity associated with the recovery of the response function and the exposure ratios. We show that all previous methods break these ambiguities only by making assumptions on the form of the response function. While iterative schemes which may not converge were used previously to find the exposure ratio, we show when it can be recovered directly from the brightness transfer function. We present a novel method to recover the brightness transfer function between images from only their brightness histograms. This allows us to determine the brightness transfer function between images of different scenes whenever the change in the distribution of scene radiances is small enough. We show an example of recovery of the response function from an image sequence with scene motion by constraining the form of the response function to break the ambiguities.