a model for the d evelopment of analytical information ... · cultural heritage are objects of...

Science and Technology for Art Conserving and Recording Tangible, Intangible, and Natural Heritage

Summer School 4-7 September 2012, Manila, Philippines

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A model for the development of analytical information database: A case study on Japanese

pigments and dyes

1. Introduction

Cultural heritage are objects of precious nature possessing tangible and intangible attributes of

a group or society that has been passed down through generations. One important aspect related to the

study of these objects has something to do with its material properties. For example in the case of

Japanese cultural heritage, pigments and dyes are ubiquitous. In this paper, a database of analytical

information on hundreds of Japanese pigments and dyes is introduced. The characterization techniques

used are classified either as spectroscopy or imaging. These techniques are used to acquire different

kinds of material information such as spectral reflectance, color information, crystallinity, elemental

composition, and spatial information. In addition, the concept of analytical imaging is introduced. The

tools and techniques used are spectrometer, XRD, SR-XRF, SEM and high-resolution multispectral

imaging. The sample preparation method is described and selected results are discussed. These material

information are collected to deepen the understanding of the mechanism of color formation and material

degradation of pigments and dyes. This could help in the efforts geared towards preservation,

conservation and restoration of cultural heritage.

2. Spectral Reflectance and Colorimetric

a. Sample Preparation and Measurement

The spectral reflectance and color information of the pigments and dyes were measured using

spectrometers. The color information such as CIELAB, CIEXYZ and sRGB were measure using an

X-rite SP60 Portable Sphere Spectrophotometer. This device could measure opacity, color strength in

chromacity, apparent and tri-stimulus calculations, and 555 shades sorting for precise color control of

products involving plastic, painter or textile materials [1]. It could also do simultaneous measurements

of both specular-included (color) and specular-excluded (appearance) which could determine the

influence of the specular component. The samples used for measurement are pigments and dyes painted

on paper which were prepared in the traditional Japanese way. The samples were part of a pigment and

dye book prepared by a master Japanese painter.

On the other hand, the spectral reflectances were measure using a mini spectrometer C10083MD

made by Hamamatsu Photonics Co. The samples were measured in the range of 320 nm to 1000 nm at

45/0 geometry at 5nm and 10nm intervals. Halogen lamp was used as the light source and barium sulfate

was employed as the white standard. The spectral reflectance of the target r( ) is obtained through the



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following equation:

)()(

)()()(

dw

dtr

where is the wavelength, t( ) is the sensor response from the light reflected by the target, w( ) is

the sensor response from the light reflected by the white standard, and d( ) is the dark current of the

sensor.

b. Selected Results

Figure 1 shows a selection of Japanese pigments made into a color chart and Figure 2 shows the

corresponding spectral reflectance. This pigment chart has been used extensively as learning sample for

pigment estimation and spectral reflectance reconstruction of pigments and dyes on Japanese artworks

[2-30].

Figure 1: Patches of Japanese pigments and dyes which are part of the database



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Figure 2: Spectral reflectance corresponding to the pigments and dyes in Figure 1.

3. X-ray Diffraction


X-ray diffraction is a very useful nondestructive method for studying crystal structure of

materials. It can be used to detect the phases present in samples and provide information on the physical

state of the sample, such as grain size, texture and crystal perfection [31]. X-ray diffraction peaks are

produced by constructive interference of monochromatic beam scattered from each set of lattice planes

at specific angles. The peak intensities are determined by the atomic decoration within the lattice planes.

Consequently, the X-ray diffraction pattern is the fingerprint of periodic atomic arrangements in a given

material. An on-line search of a standard database for X-ray powder diffraction pattern enables quick

phase identification for a large variety of crystalline samples [32]. The spacing in the crystal lattice can

be determined using Bragg’s law given by the equation below:

2 sinn d

Where n is the diffraction order, λ is the wavelength, d is the spacing between the planes in the atomic

lattice, and θ is the angle between the incident ray and the scattering planes.

The experiment for creating the XRD information database was conducted using X-ray

diffractometer (MultiFlex, Rigaku) at the Nanometrics Laboratory, Department of Micro Engineering,

Kyoto University. The pigment and dye samples used for the measurements come in two different forms.

The samples are measured either in powder form or painted on paper form. The painted samples were

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0.1

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0.3

0.4

0.5

0.6

0.7

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0.9

1

350 400 450 500 550 600 650 700 750 800 850

Wavelength [nm]

Refl

ecta

nce



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either taken from the pigment book prepared by traditional Japanese painting master or prepared in the

lab using traditional preparation method. 2 scanning was performed at 75152 with CuKα

line (wavelength: 0.154 nm) at 40 kV and 40 mA. The sampling width and the scanning speed were

0.02º and 3.00º/min respectively.

b. Selected Results

Figure 3 shows representative XRD results. Two pigments were chosen as representative sample

namely malachite (a) and azurite (b). These pigments are widely used in Japanese artworks to depict

green and blue hues. The XRD of malachite shows the influence of sample preparation. The selected

samples have the same grain size but prepared three different ways: B: from the pigment book; P:

prepared in the lab and painted on Japanese paper; and P’: in powder form from a Japanese pigment

supplier (ナカガワ胡粉絵具). On the other hand, the XRD of the selected azurite samples shows the

influence of powder grain size.

(a)

(b)

Figure 3: Representative XRD peaks of malachite (a) and azurite (a).

4. Synchrotron Radiation X-ray Fluorescence




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Synchrotron radiation (SR) analyses were performed at beam line 4A of the Photon Factory in

Tsukuba, Japan. The electron beam energy in the storage ring was 2.5 GeV, with a maximum current of

400 mA. Incident X-ray energy was 15 keV. The cross-section of the beam was approximately 1(v) x

1(h) mm2 on the sample. The synchrotron radiation was monochromated by a multilayered reflecting

mirror. Precise beam size of monochromated X-rays was adjusted using slits. The incident and

transmitted X-rays were monitored by ionization chambers that were set in front of and behind the

sample. The fluorescent X-rays were collected by a solid-state detector at 90 degrees to the incident

beam. Measurements were performed in air. Point spectra were measured for obtaining consistent

elements of the samples. The spectra were obtained by using a multi-channel analyzer. The measurement

time was 100 seconds for each spectrum. The collected data were used to analyze SR-XRF spectra. The

pigment and dye samples were measure either in powder or painted form.

b. Selected Results

XRF spectra were measured to collect accurate information on the elemental composition of the

pigments and dyes in the database. Figure 4 shows the XRF spectra of representative Japanese pigments.

(a)Azurite (b) Malachite

(c) Cinnabar (d) Hematite

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Cu KCu K

Cu esc

Pb L

0 5 10 15

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10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Cu KCu K

Cu esc

0 5 10 15

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10

100

1000

10000

100000

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nsity [a

.u.]

Energy [keV]

Hg L

Hg L

Hg L

Hg LHg L

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10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Cu KPb L

Fe K

Fe K



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(e) Ocher (f) Jade

(g) Vermilion (h) Lapis Lazuli

(i) Coral Powder (j) Minium

0 5 10 15

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10

100

1000

10000

100000

In

ten

sity [a

.u.]

Energy [keV]

Cu K

Pb LSi K

Fe K

Fe K

Ar K

Ca K

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Pb L

Si K

Fe KZn K

Ar K

K K

K K

Fe K

Pb L

0 5 10 15

1

10

100

1000

10000

Inte

nsity [a

.u.]

Energy [keV]

VR

VK

VO

VY

Hg L

Hg L

Hg L

Hg L

Hg L

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Pb LSi K

Fe K

Zn K

Ar K

Ca K

Ca K Fe K

Cu K

S K

K K

Ti K

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

D

E

Ca K(sum)

Zn K

Ca K

Pb L

Ca esc

Zn K

Fe K

Ca K

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Pb L

Pb L

Pb L



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(k) Sodalite (l) Agate

Figure 4: XRF spectra of representative mineral pigments used in Japanese cultural heritage

5. Scanning Electron Microscopy


SEM images were acquired on selected pigments to investigate influence of particle size and

distribution on the color formation of pigments. The pigments used for the characterization are in

powder form from a Japanese pigment supplier (ナカガワ胡粉絵具). JSM-6701F (JEOL) was

employed for SEM measurement. The acceleration voltage of the electron beam was 5 kV. The emission

-5 Pa. Every sample was

coated with Pt-PD using E-1030 Ion Sputter (Hitachi Co). The discharge current in sputtering was 13

mA, and the degree of vacuum in the chamber was 1.055 Torr. The sputtering time was 120 msec.

b. Selected Results

Figure 5 shows the SEM images of selected Japanese pigments found in the database.

0 5 10 15

1

10

100

1000

10000

100000

In

ten

sity [a

.u.]

Energy [keV]

Pb L

Si K

Fe KZn K

Ar K

Ca K

Ca K

Fe K Zn KMn K

0 5 10 15

1

10

100

1000

10000

100000

Inte

nsity [a

.u.]

Energy [keV]

Cu K

Pb L

Si K

Fe K

K K

Fe K

Ar K



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(a) Azurite (b) Jade

(c) Agate (d) Sodalite

(e) Cinnabar (f) Hematite



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(g) Ocher

Figure 5: SEM images of selected Japanese pigments found in the database

6. High-resolution multispectral analytical imaging

a. Multispectral analytical imaging (MAI)

Analytical imaging refers to the technique which uses image processing, data mining and pattern

recognition to extract useful and relevant information about different properties of a material. This is

based on the fact that a material subjected to an incident electromagnetic radiation behaves in a

predictable and quantifiable way. A simple radiation-matter interaction model is depicted by Figure 6.

The characteristic material response depends on the energy and frequency of the radiation. In this case,

the focus was given within the visible- near infrared range of the electromagnetic spectrum. The material

response is quantified based on its spectral properties, colorimetric information and spatial features. In

the proposed analytical imaging technique, it is believed that the most important aspect of an imaging

system is the acquisition of the images. Without a good quality image, any processing would be

meaningless. In this section the implementation of a nondestructive and noninvasive means of analytical

imaging capable of acquiring uninterpolated high resolution images is introduced. The aim is to

complement the analytical information gathered from more established characterization techniques

introduced in the previous sections. The imaging system is used for high-resolution multispectral

imaging. This system is composed of several key components such as light source, camera, spectral

filters and learning sample. In light of the discussion on the building of analytical information database

on pigments and dyes, the important aspects relating to the learning sample would be explored. However,

the method used for multispectral imaging would be introduced.



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Figure 6: A simple representation of the radiation and matter interaction.

Multispectral images were taken using a high-resolution flat-bed scanner designed and

developed at Kyoto University. A schematic representation of the system is depicted by Figure 7. The

scanner is capable of imaging virtually any size of object with very high spatial and spectral resolutions.

The scanning spatial resolution is categorized into three regimes. These include, low resolution, high

resolution and ultrahigh resolution imaging. Low resolution scanning produces images ranging from 300

to 600 dpi (42-85 μm/pixel); high resolution scanning starts above 600 dpi up to 1000 dpi (25 μm/pixel);

and ultrahigh resolution scans images above 1000 dpi up to 3000 dpi (8 μm/pixel). To put this in

perspective, a high-end DSLR with an out-of-the-box lens kit normally produces images at 250 dpi

while an office document scanner scans at 300 dpi then interpolates the image to produce the published

specification of higher resolution scans. Once the image is interpolated, a lot of analytical information is

lost and cannot be recovered. Another, analogy is that a true high resolution image is achieved with

optical zooming while an interpolated high resolution images is digitally zoomed. In the case of the

scanner used in this study, all the images are produced at true resolution with minimal post-processing to

preserve the analytical information saved on the pixels of the images.



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Figure 7: Schematic representation of the high-resolution scanner used for multispectral imaging.

The multispectral images were captured with a monochromatic CMOS line-camera using

spectral-cutting and band-pass filters. A total of eight images were taken which contain spectral

information from 380-850 nm. The images were used to reconstruct spectral data cubes with a 5-nm

resolution. The spectral data were then used to reconstruct spectral reflectance and colorimetric

information. Referring to the physical model shown in Figure 6, it may be inferred that the sensor

response of an imaging device when an object is irradiated with visible and near infrared radiation is

proportional to its spectral reflectance. The sensor response, characterized by the image pixels, can be

mathematically expressed as a function of the object’s spectral reflectance, camera sensitivity, light

source spectral radiance and system error. This is shown in Eq.6.1:

eCp d)()()( rL Eq.6.1

p is an M 1 sensor response vector from the M channel sensor, C(λ) is an M 1 vector of spectral

sensitivity of the sensor, L(λ) is the spectral radiance of the illumination, r(λ) is the spectral reflectance

of the object, and e is an M 1 additive noise vector. For mathematical convenience, Eq.6.1 can also

be expressed in vector form as follows:

eCLrp Eq.6.2

where C is an M N matrix of spectral sensitivity of the sensor, L is an N N diagonal matrix of

spectral radiance of the light source, and r is an N 1 spectral reflectance vector of the target. This



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expression implies that there is a linear relationship between the sensor response and spectral reflectance

of the target. Therefore the transfer function from sensor response to spectral reflectance can be

expressed as a matrix. There are several ways to solve this relationship. It can be either direct or indirect

[33]. Direct method is the most accurate technique for spectral reconstruction but requires a priori

knowledge of the light source characteristics and camera sensitivity. This is not often practical since

there is no guarantee that the sensitivity function will not change due to wear and tear of the equipment.

This warrants the regular measurement of the required spectral characteristics and sensitivity of the

imaging components. An example of a popular direct method is the Wiener estimation. Since reliability

and efficiency are required when doing measurements in situ, a more practical approach is necessary. In

this study, an indirect method of solving the vector relationship between the sensor response and spectral

reflectance is implemented. The technique is based on Moore-Penrose pseudoinverse. In this method,

the vector relationship is solved without the prior knowledge of the spectral characteristics of the system

by using a learning sample. The learning sample can be used to estimate a conversion matrix to

approximate the camera and light source spectral characteristics without having to worry about systemic

changes. This makes the method device independent.

Since the samples characterized in this study are Japanese pigments, a specially designed and

selected palette of Japanese organic and inorganic mineral pigments was used as the learning sample.

The learning sample is composed of 173 pigments. They represent a wide variety of pigments including

natural and artificial; organic and inorganic; ancient and modern; and a broad spectrum of colors with

distinct spectral sensitivities at the infrared region. These learning samples are used to estimate the

spectral reflectance. More information about the learning sample would be discussed in the next section.

Going back to Eq. 6.2, it can be rewritten as,

Hp r Eq.6.3

where H represents the camera and light source spectral characteristics and e is omitted for simplicity.

H in this case represents an M x N matrix with M being the number of spectral channels and N as the

number of spectral interval covering the desired spectral range. The pseudoinverse model is a

modification of the Wiener estimation by regression analysis [4]. In this model, a matrix W is derived by

minimizing from a known spectral reflectance of a learning sample, R, and the corresponding

pixel values, P, captured at a certain spectral band. The matrix W is given by Eq.6.4:

1)( tt PPRPRPW Eq.6.4

Where P+

represents the pseudoinverse matrix of P. By multiplying the derived matrix W to the pixel

value of the target image, p, the spectral reflectance can be estimated using Eq.6.5:



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pr W = ˆ Eq.6.5

The size of the matrices used in Eq.6.4 and Eq.6.5 is a function of the number of learning

sample k, number of multispectral bands M and number of spectral reflectances N. In this study, the

value of M and N depends on the spectral range and number of filters used. The number of filters used is

M=8 while N is either 95 for the 5-nm interval spectral reconstruction between 380-850 nm. Since the

images have high spatial and spectral resolution, the ROI for spectral reflectance reconstruction is

reliable up to pixel level. This enables spectral measurement at spatial resolutions which are not possible

with conventional spectrometers.

b. Japanese pigments and dyes as analytical imaging learning sample

The learning sample is one of the most important components of the analytical imaging system

developed in this study. As discussed in the previous section, this helps approximate the camera and

light source characteristics in reconstructing spectral reflectance and color images from multispectral

images. In a way, using a learning sample is a much simpler way of solving the linear relationship

between the recorded sensor response with the interaction between the light source and the material. In

the past, researches have used standard test color charts like the Kodak Q60, the IT8 color chart and the

Gretag-Macbeth color checker just to name a few as learning sample [34-35]. The advantage of using

these charts is that it contains wide variety of color patches. This is a good sample for colorimetry.

However, since they are not produced using natural colors, they do not possess a correct representation

of natural materials. Their usability is confined in the visible region and may therefore not be suitable

for spectroscopy beyond this region. The spectroscopic technique presented in this study can cover the

visible to the near infrared region. A uniquely designed learning sample was used to reconstruct spectral

reflectance and color image from multispectral image.

The learning sample is composed of 173 commonly used Japanese pigments which includes natural

and artificial pigments; ancient and modern; and organic and inorganic. In addition, it also contains few

dye samples. Japanese pigments are chosen as learning sample because the main target objects where the

analytical imaging system is used for is cultural heritage. The patches of Japanese pigments and dyes

were cut from a book painted by a Japanese painting master and arranged into a color chart which could

be used for scanning. A more detailed procedure on how the learning sample was prepared is described

in the Annex. Figure 8 shows how the pigments and dye patches are arranged. The first five rows are

natural mineral pigments used in the pre-Edo era. It includes copper-based pigments, mercury-based

pigments, iron-based pigments and some organic pigments. The next two rows contain old artificial

pigments and some dyes. The next two rows are natural pigments introduced much more recently and

can be classified as modern pigments (used from Edo period and beyond). Rows J to M are modern

artificial pigments and the last row are metallic pigments. Metallic pigments like silver and gold are

included in the learning sample since they are commonly used pigments especially in Japanese arts.



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Figure 8: A pictogram (a) of the learning sample and a reconstructed color image (b) of the most

commonly used Japanese mineral pigments and dyes.

As mentioned previously, the learning sample is composed of wide variety of pigments and dyes.

The pigments can be classified either according to the type or the period when it was first introduced.

Figure 9 shows the distribution of pigment according to the said classification. It can be seen that natural

and artificial pigments are well represented. Statistically, the distribution is almost 50-50. It was decided

to include both artificial and natural pigments mainly on the basis of spectral reflectance reconstruction.

It was observed that pigments of the same color and similar basic chemical composition may emit

different spectra at the near infrared region [12]. For example, copper-based pigment like malachite and

azurite produces completely different spectra for natural and artificial pigment even though the colors

are exactly the same. Since the visible spectra are the same, it is difficult to tell them apart but at the near

infrared region, they are easily distinguishable. This is an important aspect of the analytical imaging at

the near infrared region.



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Figure 9: Pie charts of the categorical distribution of the pigments in the learning sample: one is sorted

in terms of the nature and type of pigment or dye (a) and the other is sorted according to era or time

period (b) when the pigment was first introduced.

The pigment distribution is also given in terms of the period or era with respect to Japanese

history. Some pigments used can be traced way back in the pre-historic era like the Jomon and Kofun

period (~27%). However, most of them are first introduced towards the end of the first millennium when

the Japanese art has flourished through contacts between Korea and China. Since the learning sample

was mainly for Japanese arts, knowing the historical significance of the pigment is important. It is vital

that the most commonly used pigments are well represented to facilitate an accurate reconstruction.

Figure 10 shows the measured spectral reflectance of the pigments according to classification. It can be

seen that the spectra well cover the spectral region from 380-850 nm. The spectra include old artificial

and natural pigment, new artificial natural and mineral pigments, dyes and metallic pigments. In this

study old pigments refer to the eras before the Edo period while new refers to Edo up to the present.

Old Natural

Pigments;

37%

Old

Artificial

Pigments;

13%

Modern

Natural

Pigments;

10%

Modern

Artificial

Pigments;

34%

Metallic

Pigments;

4% Dyes; 2% Jomon; 2%

Kofun- Late

Kofun; 25%

Asuka-Nara;

20%

Meji; 44%

Edo; 6%Kamakura;

1%Muromachi;

2%



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Figure 10: Measured spectral reflectance of the pigments used as learning sample: (a) old natural

pigments; (b) old artificial pigments; (c) dyes; (d) modern natural pigments; (e) modern artificial

pigments; and (f) metallic pigments.

7. Summary

In this paper, the development of analytical information database of Japanese pigments and dyes was

introduced. Different characterization techniques related to material investigation has been applied to

hundreds of pigments and dyes. The techniques employed were either spectroscopic or image-based

characterization which were used to measure different material properties. These include

spectrophotometry (color information and spectral reflectance), XRD (crystallinity and structure),

synchrotron radiation- XRF (elemental composition), SEM (grain size and particle distribution) and

high-resolution multispectral imaging (analytical imaging). Focus was given to multispectral analytical

imaging as new method of characterizing pigments and dyes found in cultural heritage. The proposed

method aims to complement the information extracted from more established technique. The main

advantage of this technique is that it is very practical, mobile and could be used to analyze cultural

heritage in situ non-invasively.

8. References

1. http://www.xrite.com/product_overview.aspx?ID=249

2. Ari Ide-Ektessabi, Jay Arre Toque and Yusuke Murayama, Analysis of cultural heritage by accelerator

techniques and analytical imaging, AIP Conference Proceedings, 1412, pp 5-16, 2011.

0

0.5

1

380 580 7800

0.5

1

380 580 780 0

0.5

1

380 580 780

0

0.5

1

380 580 7800

0.5

1

380 580 780

0

0.5

1

380 580 780

Sp

ect

ral

Waveleng

(a) (b) (c)

(d) (e) (f)



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3. Jun Kaneko, Yusuke Murayama, Jay Arre Toque and Ari Ide-Ektessabi, Non-destructive analytical

imaging of metallic surfaces using spectral measurements and ultrahigh resolution scanning for

cultural heritage investigation, Proceedings of SPIE Vol. 8291,82911E (2012).

4. Jay Arre Toque and Ari Ide-Ektessabi, Investigation of the Degradation Mechanism and Discoloration

of Traditional Japanese pigments by Multispectral Imaging, Proceedings of SPIE, Vol. 7869, 78690E,

2011.

5. Jay Arre Toque, Yusuke Murayama and Ari Ide-Ektessabi, Polarized Light Scanning for Cultural

Heritage Investigation, Proceedings of SPIE, Vol. 7869, 78690N, 2011.

6. Jay Arre Toque, Masateru Komori, Yusuke Murayama and Ari Ide-Ektessabi. Analytical Imaging of

Traditional Japanese Paintings using Multispectral Images. VISIGRAPP 2009, CCIS 68, pp. 119-132,

2010. Springer-Verlag Berlin Heidelberg 2010.

7. Jay Arre Toque, Yusuke Murayama and Ari Ide-Ektessabi. Pigment Identification based on Spectral

Reflectance Reconstructed from RGB Images for Cultural Heritage Investigations. Proceedings of

SPIE, Vol. 7531, 75310K, 2010.

8. Jay Arre Toque, Yuji Sakatoku and Ari Ide-Ektessabi. Analytical Imaging of Cultural Heritage

Paintings using Digitally Archived Images. Proceedings of SPIE, Vol. 7531, 75310N, 2010.

9. J.A. Toque, M.K. Herliansyah, M. Hamdi, A. Ide-Ektessabi, I. Sopyan. Adhesion Failure Behavior of

Sputtered Calcium Phosphate Thin Film Coatings Evaluated Using Microscratch Testing. Journal of

the Mechanical Behavior of Biomedical Materials, Volume 3, Issue 4, Pages 324-330, 2010.

10. Mariona Rabionet, Jay Arre Toque and Ari Ide-Ektessabi. In vitro evaluation of osteoblast-like cells

on hydroxyapatite-coated porous stainless steel implants by synchrotron radiation X-ray fluorescence.

X-Ray Spectrom. 2009, 38, 278 – 282.

11. Jay Arre Toque, Yuji Sakatoku and Ari Ide-Ektessabi, Pigment Identification by Analytical Imaging

using Multispectral Images, Proceedings 2009 IEEE International Conference on Image Processing

(ICIP ’09), pp. 2861-2864, 2009.

12. Jay Arre Toque, Y. Sakatoku, Julia Anders, Yusuke Murayama and A. Ide-Ektessabi, Multispectral

Imaging: the influence of lighting condition on spectral reflectance reconstruction and image

stitching of traditional Japanese paintings, Proceedings of IMAGAPP 2009- International Conference

on Imaging Theory and Applications, pp.13-20, 2009.

13. Jay Arre Toque, Y. Sakatoku, Julia Anders, Yusuke Murayama and A. Ide-Ektessabi, Analytical

imaging of cultural heritage by synchrotron radiation and visible light-near infrared spectroscopy,

Proceedings of IMAGAPP 2009- International Conference on Imaging Theory and Applications,

pp.121-128, 2009.

14. Y. Sakatoku, Jay Arre Toque, and A. Ide-Ektessabi, Reconstruction of hyperspectral images based on

regression analysis: optimum regression model and channel selection, Proceedings of IMAGAPP

2009- International Conference on Imaging Theory and Applications, pp.50-54, 2009.



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15. Jay Arre Toque and Ari Ide-Ektessabi. Reconstruction of elemental distribution images from

synchrotron radiation x-ray fluorescence. International Journal of Modern Physics B, Volume 23, No.

4, 557-569, 2009.

16. Ari Ide-Ektessabi, Jay Arre Toque, Kim Min, Yusuke Murayama and Chizu Hoshiai, Analysis of

cultural heritage by synchrotron radiation and accelerator mass spectroscopy, 11th

International

Conference on Applications of Nuclear Techniques, Crete, Greece, Jun 12-18, 2011.

17. Ari Ide-Ektessabi, Jay Arre Toque and Yusuke Murayama, Mesoscopy: A new approach for industrial

in-line inspection, Proceedings of the 4th

Regional Conference on Manufacturing, Yogyakarta,

Indonesia, Nov 9-10, 2011.

18. J.A. Toque and A. Ide-Ektessabi, Characterization techniques for investigating the degradation

mechanisms of traditional Japanese pigments, Technart 2011, Berlin, Germany, April 26-29, 2011.

19. Y. Murayama, J.A. Toque and A. Ide-Ektessabi, High-resolution polarized scanning for analyzing

Japanese folding screens with gold and silver foils, Technart 2011, Berlin, Germany, April 26-29,

2011.

20. G.H. Takaoka, J.A. Toque, C. Hoshiai, A. Ide-Ektessabi, A Cultural and Spiritual Bridge between

Japan and Korea, International Conference on Entertainment Computing, Seoul, Korea, 2010.

21. Ari Ide-Ektessabi and Jay Arre Toque, Image Acquisition Devices for Archiving and Analyzing

Cultural Heritage, The 2nd

AUN/SEED-Net Regional Conference on Manufacturing Engineering,

Bandung, Indonesia, 7-8 December 2009.

22. Ari Ide-Ektessabi and Jay Arre Toque, Application of Analytical Imaging Techniques for

Investigating Cultural Heritage, The 45th

Annual Conference on X-ray Chemical Analysis, 5-6

November 2009, Osaka, Japan, 2009.

23. Jay Arre Toque and Ari Ide-Ektessabi, Characterization of Mineral Pigments used in Traditional

Japanese Paintings, Proceedings of the 27th

Samahang Pisika ng Pilipinas Physics Congress,

Tagaytay, Philippines, SPP-2009-025, 2009.

24. Jay Arre Toque, Yuuichi Murata and Ari Ide-Ektessabi. Effects of High Temperature Heating on the

Discoloration of Traditional Japanese Pigments. Report on Nanotechnology Support Project 2008,

Kyoto University, H20-043.

25. Jay Arre Toque, Ryoichi Nishimura, Ari Ide-Ektessabi. Analysis of cultural heritage by synchrotron

radiation and visible light-near infrared spectroscopy. Photon Factory Activity Report 2007, #25 Part

B, page 255, 2008.

26. Jay Arre Toque, Yuji Sakatoku, A. Ide-Ektessabi. Analytical imaging of materials using multispectral

images. AUN-SEED Net International Symposium, October 23, 2008, JICA Research Institute (Tokyo,

Japan).

27. Jay Arre Toque, Yuji Sakatoku, Masateru Komori, Yusuke Murayama and Ari Ide-Ektessabi.

Analytical Imaging of Cultural Heritage. 1st AUN/SEED-Net Regional Conference in Manufacturing

Engineering, Manila, Philippines, Nov 2008.



19

28. Ari Ide-Ektessabi, Jay Arre Toque, Julia Anders and Yuji Sakatoku. Design and construction of a

Total System for High-Resolution Digitization, Non-destructive Analysis and Secure Dynamic

Display of Cultural Heritage. 1st AUN/SEED-Net Regional Conference in Manufacturing

Engineering, Manila, Philippines, Nov 2008.

29. Yuji Sakatoku, Tokuyama Hirokazu, Jay Arre Toque, Julia Anders, Hitoshi Kubota, Yuichi Murata,

Min Kim, Saeko Yamaguchi, Masakazu Kimura, Yoko Kasajima and Ari Ide-Ektessabi. High

resolution imaging system for cultural heritage. 11th

Buddhism Fine Arts History Meeting 2008,

Korea, April 2008.

30. Jay Arre Toque and Ari Ide-Ektessabi. Analytical imaging of single neurons derived from synchrotron

radiation x-ray fluorescence spectra. 9th International Conference on Applications of Nuclear

Techniques, Crete, Greece, June 2008.

31. G. Rhodes, Crystallography Made Crystal Clear, Academic Press. C.A. 2000.

32. Joint Committee on Powder Diffraction Standards (JCPDS), International Center for Diffraction Data,

Swathmore, PA.

33. Shimano N (2006) Opt. Eng. 45, 013201.

34. Shimano, N., Terai, K., Hironaga, M.: Recovery of spectral reflectance of objects being imaged by

multispectral cameras. Journal of Optical Society of America A, 24(10), 3211-3219 (2007).

35. Y. Murakami, K. Fukura, M. Yamaguchi, N. Ohyama, Optics Express, 16(6), 4106-4120 (2008)

a model for the d evelopment of analytical information ... · cultural heritage are objects of...

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