Fundamentals of digital image processing
Fundamentals of digital image processing
International Journal of Computer Vision
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Proceedings of the 25th 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
Robot Vision
What Can Be Known about the Radiometric Response from Images?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Photometric Calibration of Zoom Lens Systems
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
Video orbits of the projective group a simple approach to featureless estimation of parameters
IEEE Transactions on Image Processing
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
Blind inverse gamma correction
IEEE Transactions on Image Processing
A Simple Self-Calibration Method To Infer A Non-Parametric Model Of The Imaging System Noise
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Superresolution under photometric diversity of images
EURASIP Journal on Applied Signal Processing
Computer Vision and Image Understanding
Object matching in disjoint cameras using a color transfer approach
Machine Vision and Applications
A Visual Perception Approach for Accurate Segmentation of Light Profiles
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Registration of joint geometric and radiometric image deformations
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Joint radiometric calibration and feature tracking system with an application to stereo
Computer Vision and Image Understanding
Robust movement detection based on a new similarity index for HDR imaging
ACM SIGGRAPH 2010 Posters
ADICT: accurate direct and inverse color transformation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Fast patching of moving regions for high dynamic range imaging
ACM SIGGRAPH ASIA 2010 Posters
Bottom-up segmentation for ghost-free reconstruction of a dynamic scene from multi-exposure images
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Joint photometric and geometric image registration in the total least square sense
Pattern Recognition Letters
Joint spatial and tonal mosaic alignment for motion detection with PTZ camera
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Reference-guided exposure fusion in dynamic scenes
Journal of Visual Communication and Image Representation
A Fast and reliable image mosaicing technique with application to wide area motion detection
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Exposure stacks of live scenes with hand-held cameras
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Nonuniform lattice regression for modeling the camera imaging pipeline
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Optimized hierarchical block matching for fast and accurate image registration
Image Communication
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An image acquired by a camera consists of measured intensity values which are related to scene radiance by a function called the camera response function. Knowledge of this response is necessary for computer vision algorithms which depend on scene radiance. One way the response has been determined is by establishing a mapping of intensity values between images taken with different exposures. We call this mapping the intensity mapping function. In this paper, we address two basic questions. What information from a pair of images taken at different exposures is needed to determine the intensity mapping function? Given this function, can the response of the camera and the exposures of the images be determined? We completely determine the ambiguities associated with the recovery of the response and the ratios of the exposures. We show all methods that have been used to recover the response break these ambiguities by making assumptions on the exposures or on the form of the response. We also show when the ratio of exposures can be recovered directly from the intensity mapping, without recovering the response. We show that the intensity mapping between images is determined solely by the intensity histograms of the images. We describe how this allows determination of the intensity mapping between images without registration. This makes it possible to determine the intensity mapping in sequences with some motion of both the camera and objects in the scene.