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<p>Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes, such as edge detection and shape from shading. Using physical models for charged-coupled device (CCD) video cameras and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and scene variation. This analysis forms the basis of algorithms for camera characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of camera noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results confirm that the models presented in this paper are useful for modeling the different sources of variation in real images obtained from video cameras.</p>
calibration; video cameras; reflectivity; computer vision; CCD image sensors; edge detection; radiometry; parameter estimation; semiconductor device noise; semiconductor device models; radiometric CCD camera calibration; noise estimation; primary cue; visual processes; edge detection; shape from shading; video cameras; material reflectance; digitized pixel values; sensor noise; scene variation; camera characterization; scene description; fixed pattern nonuniformity; spatial variation; dark current

R. Kondepudy and G. Healey, "Radiometric CCD camera calibration and noise estimation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 267-276, 1994.
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