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.
Hyperspectral imaging provides us with high-dimensional, scene-independent color information but faces problems such as long image acquisition time and severe lighting and focusing conditions. To achieve efficient spectral imaging, this study presents an extended method of Bayesian image superresolution. The proposed method increases both the spatial and the wavelength resolution of input images and enables the processing of hyperspectral images to a higher resolution from easily acquired low-spatial-resolution multispectral images. In an experiment using acquired multispectral images of a Japanese traditional painting, visible and near-infrared hyperspectral images were produced, and the obvious effect of superresolution was validated.
This paper presents a new approach for analyzing spectroscopic characteristic of metallic surfaces using spectroscopic
and image analysis. This method is useful for contactless and non destructive analysis of cultural heritage. Spectral
luminance, CIELAB, and CIEXYZ value of more than 80 metallic surfaces were measured with spectrometer and
scanned to examine spectroscopic characteristics of foils by using multiband images. This analysis through imaging can
improve the method for extracting difference related to types of metallic foils. Firstly, the spectral reflectance of each foil
was measured ranging from 220 to 850 nm in steps of 1 nm. The images were captured with color and monochromatic
camera using color filter in order to analyze the targets by multispectral approach. Then, principal component analysis
(PCA) was conducted with image pixel value of each target. The results have shown that the spectral reflectance whose
peak and change rate at a particular wavelength region differed from each foils, and that the multispectral images
extracted the difference in spectral characteristics related to different types of metallic foils and Japanese papers. This
could be useful in distinguishing among foils. This provides some promise that unknown metallic foils can be identified
through the measurement of their spectroscopic features.
This study presents a novel method which applies superresolution to hyperspectral image reconstruction in order
to achieve a more efficient spectral imaging method. Theories of spectral reflectance estimation, such as Wiener
estimation, have reduced the time and problems faced in spectral imaging. Recently Wiener estimation has
been extended to increase not only the spectral resolution but also the spatial resolution of a hyperspectral
image by combining the methods for image deblurring. However, there is a demand for more efficient spectral
imaging techniques. This study extended the Wiener estimation further to achieve superresolution beyond simple
deblurring because superresolution has more advantages: the possibility of getting higher spatial resolution,
and the automatic registration of multispectral images. Maximization of the marginal likelihood function is
employed in this method to reconstruct the high resolution hyperspectral image on the basis of Bayesian image
superresolution. The obvious effect of superresolution was validated through an experiment using acquired
multispectral images of a Japanese traditional painting.
Numerous cultural heritage art works have shiny surfaces resulting form gold, silver, and other metallic pigments. In
addition varnish overlayer on oil paintings makes it challenging to retrieve true color information. This is due to the
great effect of lighting condition when images are acquired and viewed. The reflection of light from such surfaces is a
combination of the surface's specular and diffused light reflections. In this paper, this specific problems encountered
when digitizing cultural heritage were discussed. Experimental results using the images acquired with a high-resolution
large flat bed scanner, together with a mathematical method for processing the captured images were presented and
discussed in detail. Focus was given in separating the diffused and specular components of the reflected light for the
purpose of analytical imaging. The mathematical algorithm developed in this study enables imaging of cultural
heritage with shiny and glossy surfaces effectively and efficiently.
Common analysis techniques for artworks, such as X-ray based techniques, usually employ high-energy radiation
sources. It also oftentimes requires the removal of material from the sample making the analysis relatively destructive.
This is unacceptable for samples with high cultural value. Therefore, there is a need to develop alternative
nondestructive and noninvasive analysis methods. This paper presents an approach for pigment estimation of Japanese
paintings. Reflectance spectra were reconstructed from the RGB values of digital images with the help of multiple linear
regression analysis. A reference database with the measured reflectance spectra of the most common pigments used in
Japanese artworks was developed and used for identification by comparison and matching. Results have shown that
estimation can be successfully performed with only 2% error. The estimation results show some promise that the system
could become a powerful tool for the analysis of cultural heritage.
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