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Abstract
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 modelling 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. |