KEYWORDS: RGB color model, Reflectivity, Cameras, Statistical modeling, Data modeling, Sensors, Spectral models, Imaging devices, Color imaging, Color difference
We proposed an improved method for camera metamer density estimation. Camera metamer is a set of spectral
reflectance of object surface which induce an identical RGB response of a color imaging devices such as a digital
color camera and scanner. It is desirable for high fidelity color correction to calculate the set of metamers
and then choose the optimal value in a standard color space. Previous methods adopted too simple models
to represent the constraint of spectral reflectance. The set of metamers were over-estimated and it declined
the accuracy of color correction. We modeled the constraint of spectral reflectance as an identical ellipsoidal
Gaussian mixture distribution, and tested and compared the proposed model and two conventional models in
a numerical experiment. It was found that the proposed model can represent accurately the underlying caved
patterns within the given dataset and avoid generating inappropriate camera metamers. The accuracy of color
correction was also evaluated supposing two commercial cameras and two standard illuminants. It was shown
that higher accuracy color correction was achieved by adopting the proposed model.
KEYWORDS: Cameras, Line scan cameras, Calibration, 3D image reconstruction, Line scan image sensors, Image sensors, 3D modeling, Cultural heritage, Image resolution, 3D image processing
A Line-scan camera based stereo method for high resolution 3D image reconstruction is proposed. The imaging model of
a line scan camera is addressed in detail to describe the relationship between the coordinate of a physical object in space
and the coordinate of its image captured by the scanner. Affine-SIFT feature detector is utilized for establishing dense
stereo correspondence. Experimental result demonstrates the effectiveness and merit of this method to high resolution
digitization of cultural heritages.
We proposed a Bayesian method for estimating the system spectral sensitivities of a color imaging device such
as a scanner and a camera from an acquired color chart image. The system sensitivities are defined by the
product of spectral sensitivities of camera and spectral power distribution of illuminant, and characterize color
separation. In addition we proposed a scheme for predicting the optimal filter to increase color accuracy of
the device based on the estimated sensitivities. The predicted filter is attached to the front of camera and
modifies the system spectral sensitivities. This study aimed to improve color reproduction of the imaging
device in practical way even if the spectral sensitivities of the device are unknown. The proposed method is
derived by introducing the non-negativity, the smoothness and the zero boundaries of the sensitivity curves as
prior information. All hyperparameters in the proposed Bayesian model can be determined automatically by
the marginalized likelihood criterion. The modified system sensitivities and their color accuracy are predicted
computationally. An experiment was carried out to test the performance of the proposed method for predicting
the color accuracy improvement using two scanners. The average color difference was reduced from 3.07 to 2.04
and from 2.11 to 1.77 in the two scanners.
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