Statistical calibration of CCD imaging process

Charge-Coupled Device (CCD) cameras are widely used imaging sensors in computer vision systems. Many photometric algorithms, such as shape from shading, color constancy and photometric stereo, implicitly assume that the image intensity is proportional to scene radiance. The actual image measurements deviate significantly from this assumption since the transformation from scene radiance to image intensity is non-linear and is a function of various factors including: noise sources in the CCD sensor, as well as various transformations occurring in the camera including: white balancing, gamma correction and automatic gain control. This paper illustrates how careful modeling of the error sources and the various processing steps enable us to accurately estimate the “response function”, the inverse mapping from image measurements to scene radiance for a given camera exposure setting. It is shown that the estimation algorithm outperforms the calibration procedures known to us in terms of reduced bias and variance. Further, we demonstrate how the error modelling helps us to obtain uncertainty estimates of the camera irradiance value. The power of this uncertainty modeling is illustrated by a vision task involving High Dynamic Range image generation followed by change detection. Change can be detected reliably even in situation where the two images (the reference scene image and the current image) are taken several hours apart