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values. This method is particularly useful either when it is
crucial to transform a particular color with great accuracy or
when the calibration data is incomplete.
Ostromoukhov et al (1994) performed 3D-transformations
between RGB and CIE XYZ color spaces in order to calibrate
electronic display systems.
Mohd and Kirby (1997) tested geometric accuracy of desktop
publishing scanners by using a calibrated grid plate. They
applied affine transformation and polynomial regression
between scanned Cartesian coordinates and the calibrated ones.
They found out that 2nd and 3rd order polynomials are suitable
for geometric improvement of scanned images. Higher order
polynomials are no more useable. The order of polynomial
depends on the scanner used.
Noriega ct al (2001) determined some properties of scanners by
using negative and positive density measurements on the
scanned RGB values and the CIE XYZ color space values.
They used standard observer color matching functions to
transform the density values into CIE XYZ values. They
concluded that the inaccuracies of the colorimetric values by
scanner depend on the properties of the device and the related
color management system.
In the models based on polynomial regression, including more
samples in this part of the colorant space could reduce the errors
at the gamut boundary. Common test targets such as the ISO
12640 and 12641 have only a limited number of patches at the
gamut boundary. Therefore, Green (2000) proposes a new
target for defining media gamut boundaries. He used 2nd order
polynomial regression in his application.
Kang (1997) mentioned that many scientists have successfully
applied regression method for transformation of scanned RGB
values to colorimetric values. He determined polynomial
equations of different order that convert the RGB values to CIE
XYZ and CIE Lab color space values. He used IT8.7/2
calibration card and Sharp JX 450 scanner. He compared the
results obtained by different order of polynomials and different
standard illuminants.
Berns and Shyu (1994 and 1995) proposed a color mixing
method based on theories of Beer-Bouger and Kubelka-Munk
and scanner signals. They also applied polynomial regression.
Hardeberg (1999) developed methods based on linear
regression, polynomial regressions of 2nd and 3rd order. He
applied these methods to AGFA Arcus 2 scanner with AGFA
IT8.7/2 calibration card.
Yilmaz (2002) applied conformal, affine, projective and
polynomial transformations for reducing color inaccuracies. He
examined five different scanners. He found out that the
polynomial regression of 3rd order delivers the best results in
general.
3. SCANNERS
À scanner is an electronic device that captures the image of any
object and converts it into a computer format. It includes an
array of cells that are optically sensitive. These cells called
CCD (Charge Coupled Device) are connected with a detector
that measures the intensity of coming light and converts it to
electrical signals. The reflecting light from the object is
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B5. Istanbul 2004
projected to the detector via an optical system. The electrical
signals generated by the detector are then converted to digital
information. Any of the information represents a pixel, and its
size is a number of bits that are saved per pixel. If the number
of bits per pixel increases, the quality of image becomes better.
According to usage areas, scanners can be classified as follows
(Baltsavias & Bill, 1994):
e Photogrammetric scanners
e Scanners of large documents
e Microdensitometers
* Desktop publishing scanners
e — Other scanners such as scanners of 3-D objects, slide
scanners, text document scanners, multiple purpose
scanners, etc.
According to working principle two types are distinguished:
Drum scanners and flat bed scanners. Drum scanners can
process large format documents and delivers accurate images.
The most common type of flat bed scanners is desktop
publishing scanners.
The reliability of scanners depends on the certain tests. Four
types can be performed (Yakar, 2002).
l. Geometric accuracy: With this test geometric
distortions are investigated. Calibration targets with
maximal positional error of 2-3 micron are required.
2. Geometric resolution: This is the test of optical
scanning system. A variety of calibration targets are used.
3. Radiometric accuracy: This is the test of the
sensibility of scanner to gray tones.
4. Color accuracy: In this test the original colors and the
scanned colors are compared. Calibration cards or targets
with standardized colors are required.
After these tests it is decided if the tested device is suitable for
certain tasks.
4. COLOR CALIBRATION CARDS
Color calibration cards used for scanner calibration and test for
color accuracy have been known as Q-60 calibration cards.
They are mainly used in printing industry, photography, and for
calibration of monitors, printers and scanners. The main
purpose is to have best color outputs on reflection and
transmission materials. They are also used for color calibration
of scanners. Most of the cards, which are based on the Kodak’s
Q-60 color calibration card, are produced according to ANSI
and ISO standards.
Kodak’s Q-60 calibration cards, having all colors of the CIE
Lab color space, are in accordance with the ANSI IT8 7.1 and
[T8 7.2 and ISO 12641 standards. In general there are two
types: IT8 7.1 (transmission) and IT8 7.2 (reflection). IT8 7.1 is
printed on “Ektachrome Professional” films, with the size of
4x5 inch. IT8 7.2 is printed on “Ektachrome Professional”
cards. Its dimensions are 5x7 inch. Both consist of 240 color
parts, 24 gray parts and a human face. The colorimetric values
of the cards are given by the manufacturer in a text file. In these
files, CIE XYZ color space values, CIE Lab color space values,
Sx, Sy, Sz (standard deviations of CIE XYZ values), average
density (D) and SD (standard deviation of average density) are
given. In this study Kodak's IT8 7.2-1993 2001:02 calibration
card was used.