report masters thesis - engineering physics 30...

UMEA UNIVERSITY Manne AlstergrenDepartment of Physics December 7, 2014Masters Thesis - Engineering Physics 30 hpReport

Report

Masters Thesis - Engineering Physics 30 hp

Development of a spectral and goniophotometric imaging

measurement system for optical characterization of materials

StudentManne Alstergren

SupervisorsMattias Andersson & Niklas Johansson

ExaminerOve Axner

Abstract

In this thesis, a novel color measurement system has been developed and its performance eval-uated. It is a modular spectral imaging goniophotometer that measures angle resolved spectralreflectance and transmittance of materials, e.g. textiles, papers, coated materials and flexibleelectronics. It is a highly flexible system that can, unlike most commercial systems, capturereflectance images of both high spectral and spatial resolution over a wide range of measurementgeometries.

It is important that the system can produce geometrically correct images of the samples, as well ascapture spectral information with high accuracy, i.e. the system needs to be both geometricallyand photometrically calibrated. A line-scan spectral camera and a high accuracy translationstage provide 2D-scans of the sample, where imaged area, sample position and measurementgeometry is controlled by a high precision robotic arm. Assessment of spectral and spatialresolution, characterization of the light source and spectral accuracy of the camera are someof the evaluations carried out. Spectral accuracy, and thus color measurement accuracy, isevaluated with the color difference formula CIE ∆E∗ab. During these evaluations the system isable to measure colors at ∆E∗ab < 14 and ∆E∗ab < 30 for a matte and glossy sample respectively,both of which are standard color calibration samples. These results show large color differencesthat are most likely caused by the low signal-to-noise ratio for short wavelengths due to thelight source. Finally, the system’s angle resolved measurement capability is demonstrated bymeasurements of goniochromatic samples whose color shifts with the viewing angle. The changein color is successfully captured for viewing angles in the range of 0 to 45 degrees from the surfacenormal.

This system provides unique opportunities to analyze light scattering in materials, e.g. solarcells, thin films or printed media and will bridge the gap of modern industrial measurementneeds for material and surface characterization.

Sammanfattning

I detta examensarbete har ett nytt system for fargmatning utvecklats och dess prestanda utvarderas.Det ar en modular och spektralt avbildande goniofotometer som tar vinkelupplosta spektral re-flektans och transmittans matningar av material, som t.ex. textilier, papper, belagda materialoch flexibel elektronik. Det ar ett mycket flexibelt system som kan, till skillnad fran de flestakommersiella system, ta reflektansbilder av bade hog spektral och spatial upplosning over ettbrett spektrum av matningsgeometrier.

Det ar viktigt att systemet kan producera geometriskt korrekta avbildningar av prover, samtfanga spektral information med hog noggrannhet. D.v.s. att systemet maste vara bade ge-ometriskt och fotometriskt kalibrerat. En spektral linje-kamera och ett stegbord med hog nog-grannhet ger 2D-skanningar av provet, dar matomradet, provposition och matgeometri styrs aven robotarm. Spektral och spatial upplosning, karakterisering av ljuskallan och kamerans spek-trala noggrannhet ar nagra av de utvarderingar som utforts. Den spektrala noggrannheten, ochdarmed noggrannheten for fargmatning, utvarderas med fargskillnadsformeln CIE ∆E∗ab. Underdessa utvarderingar har systemet matt fargskillnader pa ∆E∗ab < 14 och ∆E∗ab < 30 for ett respek-tive matt och blankt prov. Dessa resultat visar stora fargskillnader som ar mest sannolikt orsakasav det laga signal-brusforhallande for korta vaglangder pa grund av ljuskallas position och spek-trala fordelning. Slutligen demonstreras systemets formaga att gora vinkelupplosta matningarav goniokromatiska prover, vars farg skiftar med betraktningsvinkeln. Systemet detekterarfargskiftningen framgangsrikt for betraktningsvinklar 0 till 45 grader fran ytnormalen.

Detta system ger unika mojligheter att analysera ljusspridning i material, t.ex. i solceller, tunnafilmer eller tryckta medier, och ska brygga gapet till den moderna industrins behov for material-och ytkarakterisering.

Acknowledgements

Mattias Andersson and Niklas Johansson at DPC, Mid Sweden University, for a very interestingand challenging masters thesis project. They have been of much support and help throughoutthe entire project. Thank you!

Ove Axner as my examiner at the Department of Physics, Umea University.

Ami Karlsson for support, help and ideas.

Report December 7, 2014

Contact information

Student Manne Alstergren [email protected] 070-217 60 95Supervisor Mattias Andersson [email protected] 070-395 78 54Supervisor Niklas Johansson [email protected] 0660-578 61Examiner Ove Axner [email protected] 090-786 67 54

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Summarized results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Theory 22.1 ”From photon to color” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Color models (CIE standards) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 CIE RGB Color matching functions . . . . . . . . . . . . . . . . . . . . . 52.2.2 CIE XYZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.3 Illuminants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.4 Uniform color spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Measuring color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.1 Reflectance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.2 Standard geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Equipment 113.1 Test samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Method 154.1 Geometric calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.1.1 Region of interest and spatial resolution . . . . . . . . . . . . . . . . . . . 154.1.2 Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.1.3 Geometrically correct scans . . . . . . . . . . . . . . . . . . . . . . . . . . 164.1.4 Sharpness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Photometric calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2.1 Light source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2.2 Illumination of sample area . . . . . . . . . . . . . . . . . . . . . . . . . . 184.2.3 Spectral accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.3 Color measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.3.1 Reflectance and Color difference ∆E∗ab . . . . . . . . . . . . . . . . . . . . 194.3.2 Goniophotometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Results 205.1 Geometric calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.1.1 Region of interest and spatial resolution . . . . . . . . . . . . . . . . . . . 205.1.2 Geometrically correct scans . . . . . . . . . . . . . . . . . . . . . . . . . . 205.1.3 Sharpness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.2 Photometric calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.2.1 Light source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215.2.2 Illumination of sample area . . . . . . . . . . . . . . . . . . . . . . . . . . 225.2.3 Spectral accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

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5.3 Color measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245.3.1 Reflectance and Color difference ∆E∗ab . . . . . . . . . . . . . . . . . . . . 245.3.2 Goniophotometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6 Discussion 29

7 Conclusions 30

8 Future work 30

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1 Introduction

”For the Rays, to speak properly, have no Color. In them there is nothing else than a certainpower and disposition to stir up a sensation of this Color or that.” - Isaac Newton

Colorimetry is the science of measuring, representing and computing color in a way that takesinto account the interaction between the physical aspects of color and the physiological aspects ofhuman vision. The physical aspect of color is the spectral distribution of light, electromagneticradiation at a narrow band of wavelengths limited by receptors in our eyes. The physiological partis where these receptors are stimulated by the electromagnetic radiation and produces signalsthat the brain interprets into colors.

This is a thesis where a modular measurements system has been developed to measure thephysical aspects of colors, namely the spectral power distribution of radiation. The systemutilizes a hyperspectral camera with an operating span of 380-800 nm, a robotic arm with asample fixture to adjust the region of interest and measurement geometry, and a movable, stableand even light source.

1.1 Background

The paper- and paperboard industry is facing major changes. There is less demand on theirproducts, like for example printed newspaper as a result of the increased use of news services onthe internet. This is tightening the competition between companies and forcing them into productspecializations of existing and new products, like coated materials, thin films, textiles, and soon. This is turn puts new demands on surface and material properties, which in turn puts higherdemand on the optical characterization used to analyze these surfaces and materials.

Most optical measurement systems are either spectrally or imaging. Compared to most com-mercially available systems the system developed in this thesis can capture reflectance images ofboth high spectral and spatial resolution. When analyzing a surface, spectral differences can beanalyzed over an entire image. The spectral information can then be converted into color valuesof suitable color space. This provides new and unique capabilities to analyze light scatteringin materials which is useful for characterizing future materials such as thin films, textiles, solarcells, new printed media, and so on. As development goes further and further in these areas, sodoes the need for optical material analysis, which this system provides.

1.2 Summarized results

The system can provide geometrically correct scans with high sharpness in both vertical andhorizontal direction. The light source is stable over time and provides an even illuminationof the sample area, which simplifies the calibration procedure. The system is calibrated forspectral offset and with reference measurements to provide accurate reflectance spectra. Colormeasurement evaluation for a matte yellow, red, green and blue sample gives a color differenceof 4 ≤ ∆E∗ab ≤ 14 and for glossy samples of the same basic colors 16 ≤ ∆E∗ab ≤ 30. While theseresults show large color differences some minor adjustments in setup, scan procedure and datamanagement should provide better results. Finally, the goniophotometric demonstration showsthat the system can resolve spectral shifts when changing angle of observation.

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2 Theory

2.1 ”From photon to color”

The fundamentals of color consists of two parts, one physical and one psychophysiological. Thephysical part starts with a light source and ends at the eye while the psychophysiological partthat starts at the eye and ends with a perceived color.

Lets begin with the physical. When excited electrons return to lower energy states they give offelectromagnetic waves/radiation that often is analogically described as photons. These photonsget a certain frequency (ν) depending on which energy state the electrons had and to whichenergy levels they descended. The frequency is given by

ν =E

h(1)

where E is the difference in energy of the states, which also is the energy of the photon, and his Planck’s constant. Corresponding wavelength is given by

λ =hc

E(2)

using

ν =c

λ. (3)

Hence, there is an inverse relation between photons energy and the wavelength of its light (λ),which means that higher energy results in a increased frequencies and shorter wavelengths. Thephotons are, depending on their energy, distributed over many wavelengths that can be observedas a spectrum. Different sources of light have different spectral distributions, I(λ). In figure 1, acouple of standardized light sources (illuminants) and their relative spectral power distributionsare shown.

Figure 1: Spectral distribution of a some example illuminants

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Each material has its own spectral reflectance R(λ). Each material will therefore reflect/absorba specific amount of a incoming spectrum I(λ) at a given wavelength. This results is a thirdspectral distribution, the reflected light E(λ), which is given by

E(λ) = I(λ)R(λ) (4)

which is what enters the eye as illustrated in figure 2.

Figure 2: Illustration of a illuminated surface where only the red part of the spectrum isreflected/re-emitted to the observer.

This is where the physical part ends and psychophysiological part begins.

The light now enters the eye. In the back of the eye are receptors called cones and rods. Thesereceptors are connected to the vision center of the brain via nerve fibers. The cones are mainlyfor color vision during normal light conditions (photoptic vision), while the rods are mainly formonochromatic vision under low illumination (scotopic vision). There are three kinds of coneswith different photosensitive pigment that absorb light in different part of the spectrum, L (long),M (medium) and S (short). The spectral sensitivity distributions functions for each of the typesare deduced from absorption experiments on extracted cones from human eyes [1] and are shownin figure 3.

Figure 3: Spectral sensitivity of the S, M, and L cones

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These spectral sensitivity functions are then used to describe color sensation in terms of tristim-ulus values.

Ltot =

∫λ

E(λ)L(λ)dλ (5a)

Mtot =

∫λ

E(λ)M(λ)dλ (5b)

Stot =

∫λ

E(λ)S(λ)dλ (5c)

Combining these tristimulus values yield two color channels (red-green and yellow-blue) and oneachromatic. As is shown in figure 4, these are in turn combined to determine the perceptualattributes of hue, saturation and brightness.

Figure 4: Model of how the S, M and L cones are combined to yield sensation of not only colorbut also hue, saturation and brightness.

The psychophysiological part will end by describing a phenomena called metamerism. Manyspectral combinations can result in the same observed color. For example: Two pieces of clothesmight appear to match in color inside the store but when taken outside into daylight the colorsare clearly not that similar. This is called metamerism and two spectral combinations thatappear to give the same color is called metamers (or metameric pair).

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2.2 Color models (CIE standards)

2.2.1 CIE RGB Color matching functions

The L, M, S functions are good representations of the spectral sensitivity areas of the cones,but they are difficult to measure and enough precision has so far not been established. So therewas need for a standardized observer and therefore, a more internationally accepted methodintroduced a three-color matching method called trichromatic matching. It is an evaluationprocess where subjects would dial in red, green and blue to match pre set colors, figure 5 [1].

Figure 5: Principle of trichromatic color matching by additive mixing of lights Red, Green andBlue, to match a pre set color. The diffusers give a uniform mix of colors for the observer toview.

In 1931 CIE (Commission Internationale de l’Eclairage, International, Comission on Illumina-tion) used this color matching experiment with monochromatic primaries at 700 nm, 546.1 nmand 435.8 nm (red, green and blue). If no color match was found, one of the primaries was movedto the other diffuser and would thus give a negative contribution as can be viewed in figure 6as negative values of the r(λ) and g(λ) curve. This resulted in the CIE RGB color matchingfunctions r(λ), g(λ) and b(λ) (CIE RGB CMFs).

Figure 6: Color-matching functions for the CIE 1931 Standard colometric observer. Consistingof monochromatic stimuli of R, G, and B (700nm, 456.1nm and 435.8nm)

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2.2.2 CIE XYZ

The CIE XYZ color matching functions, CMF, denoted x(λ), y(λ), z(λ) and shown in figure 7,include the chromatic response of the observer. It is today most used for imaging applicationsand color research.

Figure 7: Color-matching functions for the CIE XYZ Standard colorimetric observer.

CIE XYZ coordinates are linearly transformed from the CIE RGB tristimulus and are deter-mined so to avoid negative values at all wavelengths (which the CIE RGB CMFs have, seefigure 6). They are obtained by the following computations of tabulated data and spectralreflectance:

X = k

∫λ∈V (λ)

E(λ)x(λ)dλ (6a)

Y = k

∫λ∈V (λ)

E(λ)y(λ)dλ (6b)

Z = k

∫λ∈V (λ)

E(λ)z(λ)dλ (6c)

where k is a normalization factor that is constant k = 683(lumens/W ) for absolute colorimetry.For relative colorimetry, it is chosen such that Y=100 for a perfect reflector

k =100∫

λy(λ)l(λ)d(λ)

(7)

where l(λ) is the SPD of the illuminant. y(λ) is specified in a careful way so that Y is proportionalto L, the luminance of the specified color.

There are however some limitation with CIE XYZ. When estimating a color by looking at it,humans often define a color in terms the perceptual attributes of hue, saturation and lightness.CIE XYZ lacks a clear relation to these perceptual attributes and the CIE XYZ color space isneither uniform nor equidistant. This means that the same perceived color difference in differentparts of the color space does not correspond to the same euclidean distance in X, Y and Z.

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2.2.3 Illuminants

Different industries uses different illuminants, depending on what is evaluated and the applicationconditions. For example, the graphical industry uses the standard illuminant D65 which standsfor Daylight 6500K and corresponds roughly to a midday sun in western Europe. By using thisilluminant during visual evaluations, the industry can determine that prints and other productswill look as intended outdoors during the day.

CIE has defined several standard illuminants for use in colorimetry. They are specified in termsof their correlated color temperature (CCT) and are based on the temperature of a blackbodyradiator since this describes the complete SPD of the blackbody and thus its color. Four differentstandard illuminants are shown in figure 1. The CCT of an arbitrary illuminant is defined asthe color temperature of a blackbody radiator visually closest to the illuminant (in color/SPD).Sources with lower CCT tend to be more red and those with higher CCT bluer. Those withsimilar CCT are considered to render similar colors of illuminated objects but there are sourceswith identical CCT and very different SPD and thus color rendering properties.

2.2.4 Uniform color spaces

Since CIE XYZ color space is non-uniform and that uniformity is highly desired in color repro-duction systems, two uniform color spaces for practical applications were developed in 1976, bothof which are transformations of CIE XYZ tristimulus:

CIE 1976 L*a*b* (CIELAB) - Transforms from and to CIELAB is widely used in imagingapplications such as came. Lightness, L∗, is transformed solely from luminance Y, whilea∗ and b∗ depend also on X and Z respectively. a∗ corresponds to the red-green axis, b∗

corresponds to the yellow-blue axis and L∗ corresponds to the gray axis according to

L∗ = 116f

(Y

Yn

)− 16 (8a)

a∗ = 500

(f

(X

Xn

)− f

(Y

Yn

))(8b)

b∗ = 200

(f

(Y

Yn

)− f

(Z

Zn

))(8c)

where

f(x) =

{x1/3 x > 0.008856

7.787x+ 16116 x ≤ 0.008856

(9)

and Xn, Yn and Zn are the tristimuli of the white stimulus, which can be changed forwhite point corrections. This is our most uniform and equidistant color space to date butit is not perfect. This is illustrated in figure 8 where the a*b*-plane is shown. The lines inradial direction represent the hue and should be straight when going from zero (center) tofull saturation (edge). This is not the case, especially in the blue (-b*) region.

CIELUV is yet another color space. The transformation from CIE XYZ tristimulus intoCIELUV is done in a similar manner as for CIELAB, where L∗ is identical as for CIELAB.However since it is mostly used for computer screens and displays, it is not covered in detailin this thesis.

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Figure 8: Hue lines in CIELAB color space. Lines should be straight for perfect uniformity.Most non-uniform between 240 - 310.

CIE 1976 L*a*b* Color Difference ∆E∗ab - Is an evaluation method of how different twocolors are. It uses CIELAB color space, is the euclidean distance between the colors andis given by

∆E∗ab =√

(L∗2 − L∗1)2 + (a∗2 − a∗1)2 + (b∗2 − b∗1)2 =√

(∆L∗)2 + (∆a∗)2 + (∆b∗)2. (10)

Two more versions of the color difference formula exist which take the non-uniformityof CIELAB into consideration. These are called CIE 1994 color difference ∆E∗94 andCIEDE2000 color difference ∆E∗00. ∆E∗94 uses weighting functions, that depend on thereference, to vary the color intensity. ∆E∗00 is very complex, and gives only small improve-ments compared to the former. In this thesis however, only ∆E∗ab as given by equation 10is used to evaluate color difference.

2.3 Measuring color

2.3.1 Reflectance

When light hits a surface, three things can happen. It is absorbed, it is transmitted or it isreflected. The reflectance R(λ) is defined by the spectral intensity ratio, of the reflected lightj(λ) over incident light i0(λ) [2] according to

R(λ) =j(λ)

i0(λ). (11)

The reflected light is the sum of the light reflected directly at the surface and the bulk reflection,which is the light that enters the medium and is scattered until it eventually is reflected backthrough the front surface. This is illustrated in figure 9.

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Figure 9: Illustration of light reflected and scattered by a medium. i0 is the incident light and jis the reflected light that e.g. reaches an aperture.

The sensor instrument detects the reflected intensity, and by measuring the intensity of theincident light, the reflectance is obtained (absolute reflectance). However, it is difficult to mea-sure the incident light directly and therefore it is more convenient to measure the relative re-flectance.

Since the reflectance of a sample is given by the ratio

R(λ)sample =j(λ)samplei0(λ)

, (12)

and the corresponding reflectance of a reference is

R(λ)reference =j(λ)reference

i0(λ), (13)

the incident light can be assessed as

i0(λ) =j(λ)referenceR(λ)reference

. (14)

Combining equation 12 and 14 then gives the reflectance of the sample as

R(λ)sample = j(λ)sampleR(λ)referencej(λ)reference

(15)

which contains the spectral information needed to determine its color. j(λ)sample and j(λ)referenceis measured by the sensor under the same conditions, R(λ)reference is measured by an externaldevice or already given by tabulated values.

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2.3.2 Standard geometry

A measurement geometry is denoted in the form θ/θr where θ is the angle of incident light andθr is the viewing angle. Both are given in relation to the surface normal of the sample and bothhave impact on the measured reflectance (and perceived color). The two most common, shownin figure 10, are the 45/0 geometry, used by the graphical industry and in this thesis, and thed/0 geometry, used by the paper industry.

Figure 10: 45/0 is the standard geometry used by the graphical industry. d/0 (diffuse/0) is thestandard geometry used by the paper industry where the sample is illuminated by diffuse lightdue to a sphere.

To fully categorize the reflectance of an illuminated sample a function called Bidirectional Dis-tribution Function (BRDF) [3] is used. From the BRDF it is possible to extract the reflectanceunder any measurement geometry. However, goniophotometric measurements are needed toassess the BRDF and therefore, it is not applied in this thesis.

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3 Equipment

The following section gives short descriptions on the different modules and equipment used inthis thesis. The setup is shown in figure 12.

Spectral Camera PS-XX-V8E - A hyper spectral line-camera from Spectral Imaging Ltd.of model PS and version V 8E, which places it in the VIS (Visible Spectrum) category. Ithas a 23 mm F/2.4 lens and its spectral range is 380-800 nm with a spectral resolutionof 2 nm. The CCD sensor is of size 1392x1040 (spatial x spectral) at full frame, however,it is set at 1392x870. It is most sensitive in the blue part of the spectrum and is limitedto 11 frames-per-second (fps). A simple illustration of the line-camera optics is shown infigure 11.

Figure 11: Overview of the optics in the camera.

Mitsubishi Industrial Robot RV-2SDB with a CR1DA-700 series Controller - A smallrobot with six degrees of freedom (six axis), a range of 504 mm and a maximum payloadof 3.0 kg. It is used for holding and positioning the sample fixture. It controls the imagedarea, sample position and defines the measurement geometry. The base plate of the robotis placed 678 mm from the base plate of the translation stage stand.

M-403.82S Linear Translation Stage with C-663.11 Mercury Step Controller - A lin-ear translation stage can that carry up to 20 kg and can push/pull up to 50 N with aprecision of 0.1 µm/step. It is vertically mounted on a robust stand to control cameraelevation.

US-080-SF Integrating Sphere - An integrating sphere with an external tungsten-halogenlamp, a dichroic rhodium reflector and a variable attenuator fully opened. It producesuniform, stable and diffuse light, with a smooth spectral distribution. Attached to thesphere is a SDA-050-U-RTA-CX Silicone Photo diode with a detection range of 190-1100nm, which in turn is connected to a SC 6000 Radiometer and Control Instrument. It isplaced so that the opening of the sphere is 507 mm from the center of the sample area.

Computer - A Windows PC, with software for all modules above. It has SpectralDAQ forthe camera, RTtoolbox for the robot, PIMikroMove for the linear translation stage, andSC 6000 Control Panel for the SC 6000 control instrument. All data are gathered andsaved by this computer. The data is evaluated in the program Matlab with the toolboxOptprop [4] installed for color calculations.

X-rite 530 Spectrodensitometer - A hand held spectrophotometer that measures the spec-tral reflectance between 400-700 nm in an illuminated spot on a surface in the 45/0 geom-etry. In this thesis it used to measure reference reflectance data of test samples and thereference bar. It is referred to as the reference instrument in this thesis report.

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Figure 12: Overview of the system setup used for most evaluation procedures. A is the sample-to-lens distance. B is the robot arm extension. C is the distance between base plates, fixed at678 mm. D is the distance from the light source opening and center of sample area, fixed at507 mm.

X-rite 530 Spectrodensitometer - A hand held spectrophotometer that measures the spec-tral reflectance between 400-700 nm in an illuminated spot on a surface in the 45/0 geom-etry. In this thesis it used to measure reference reflectance data of test samples and thereference bar. It is referred to as the reference instrument in this thesis report.

Spectroradiometer CS-2000 - An instrument that is used to characterize the light source.

He-Ne laser - A laser that emits light at the wavelength 623.8 nm and is used to investigatethe spectral accuracy of the system.

Canon iPF6400 - This is a large format printer that is used to print sharp bars/bands forevaluation purposes.

3.1 Test samples

Printed sharp bars/bands - This sample is generated using Matlab and printed at 1200 dpion matte 160g/m2 photo paper with a Canon iPF6400 printer and is shown in figure 13.It is used to adjust the focus, evaluate sharpness, and calibrate elevation speed and steplength.

Figure 13: Black bars/bands.

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X-rite Color Checker - This is a target that comes in two types: One is called proof andhave holes cut in each color field for reference placement, while the other have the samecolor fields but it is smaller and is called classic and shown in figure 14. It is primarilyused to evaluate the system’s ability to measure color of matte surfaces.

Figure 14: X-rite Color checker classic. A matte target used for evaluation of color measurements.

Kodak Q-60 Color Input Target - This is a sample mainly used for calibration during thepreparatory process before printing and publishing with emphasis on glossy/reflective andtransparency. This is used to evaluate the system’s ability to measure color of glossysurfaces and is shown in figure 15.

Figure 15: Kodak Q-60 Color Input Target. A glossy target used for evaluation of color mea-surements.

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ChromaFlair - This is a set of samples that change color with viewing angle. They shift be-tween two main colors and come in several combinations. The shifts in color is caused bysynthetic metallic flakes that are coated with a glass-like substance. This gives the Chro-maFlair paint its vibrant metallic sparkle while the glass-like coating acts as a refractingprism, changing the apparent color through interference (like oil on water). These sam-ples are used for evaluating the systems capability for to perform angle resolved measure-ments, e.g. to detect shifts in the spectrum when viewing angle changes and are shown infigure 16.

Figure 16: Left: Set of ChromaFlair samples. Right: a sample that shifts between silver andgreen.

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4 Method

4.1 Geometric calibration

All the following geometric evaluations and adjustments are made for three different samplewidths, 210 mm, 160 mm and 100 mm.

4.1.1 Region of interest and spatial resolution

Figure 17: Evaluation of the horizontal resolution and sample-to-camera distance.

The horizontal resolution is simply given by the width of the sample resolved by the sensor,divided by the number of pixels. The resolution is then also the vertical step length to acquiregeometrically correct images. The sample-to-camera distance is acquired by marking the widthon the sample and then adjust until the markings appear just at the edges of the plot, as shownin figure 17.

4.1.2 Focus

The camera does not have any means of autofocus nor does the lens have any focus ring or anymarkings regarding distance of focal plane. However, the lens can be adjusted in or out fromits attachment ring to the camera. The attachment ring has a tightening screw to secure theposition of the lens once focus is adjusted.

To adjust the focus, a printed sample is positioned in the sample fixture with the bands/barsin a vertical position. Frames is updated and viewed using waterfall plot in the SpectralDAQsoftware, which updates the view of the camera in real time with a RGB representation. Sincethe camera is a line-camera and only captures a single line of pixels during each exposure, thetop most line in the plot updates and pushes the previous line down one step, which creates awaterfall sensation. Focusing is conducted by turning the lens in one direction or the other untilone experience the sharpest possible edges in the plot. This is illustrated in figure 18.

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Figure 18: Camera capturing the bands/bar sample and updating the waterfall plot frame byframe while adjusting the focus by turning the lens.

4.1.3 Geometrically correct scans

There are two methods of scanning a sample to produce a 2-D image. The first is by elevatingthe camera while it continuously captures frames. This will produce some motion blur thatcan be reduced by oversampling. The second, ”stepwise”, method is by elevating the camera inincrements between capturing the frames thus eliminating motion blur. It is however more timeconsuming.

It is of importance that the system can reproduce images with the same x- and y-resolution,i.e. a square on a sample is square in the scan result. This is evaluated by measuring the pixeldistance between two adjacent bands/bars, first in x-direction and then turning the sample 90◦

and scanning in the y-direction. This is illustrated in figure 19.

Figure 19: By comparing the band distance in y-direction with the band distance in x-direction,adjustment in elevation speed is made to acquire geometrically correct scans.

For continuous scans, suitable theoretical elevation speeds to start with is horizontal resolution ·fps of the camera. Any adjustment factors (if needed) is calculated by taking the resulting pixeldistance and divide it by the desired pixel distance.

For step wise scans, the step lengths are the same as the resolutions. So no further calculationsor adjustments are needed.

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4.1.4 Sharpness

No optical system is perfect and produces blur at edges, which is caused by gradual changes incolors or contrast called rise distance. Sharpness in an image can be evaluated by this and themethod is called 10-90% rise distance. It is defined as the region where the blur is 10% - 90%of the total difference between a brighter (less saturated) point and a darker (more saturated)point, illustrated in figure 20.

Figure 20: Illustration of the 10-90% rise distance.

Here it is measured by defining an interval that spans between the middle of two adjacent blackbands/bars, illustrated in figure 21, and taking the fraction of the 10-90% rise distance lengthover the length of the interval, both measured in pixels.

Figure 21: Interval of which the 10-90% rise distance is evaluated.

The sharpness is evaluated by the mean over all wavelengths and since the sharpness is fixed inthe x-direction, the goal is to get the same sharpness in the y-direction. To do this both stepwiseand continuous scans are evaluated.

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4.2 Photometric calibration

4.2.1 Light source

The light source is a stable type A illuminant that produces a uniform light through an integratingsphere. Its short- and long-term stability is evaluated by a spectroradiometer (Minolta CS 2000)to determine how the SPD changes over time. The sphere has a photoptic detector that monitorsthe light level inside the sphere in cd/m2. The monitor signal can be used to make adjustmentsfor measurements if e.g. the sample is not measured directly before or after calibration. In thisthesis, data from this detector is used to observe how long it takes for the light source to stabilizeand how much it fluctuates after it have stabilized.

4.2.2 Illumination of sample area

To evaluate how even the illumination is over the sample area, the reflected intensity of thereference is measured at five evenly spaced heights in both high and low intensity regions, asshown in figure 22 and 23. The heights are, from the top: +80 mm, +40 mm, +-0 mm, -40 mm,-80 mm.

Figure 22: Reference positions for evaluating how even the illumination is over the sample area.

Figure 23: ”Light projection” onto the sample from the light source. There is a high intensityregion closer to the light source.

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4.2.3 Spectral accuracy

Accuracy is the degree of how close a measurement is to its actual value. For this systemit is estimated with a He-Ne laser of wavelength of 623.8 nm which is directed onto a whitematte paper at approx. 45◦ angle. The obtained spectrum is then evaluated to locate the laserwavelength and determine the offset.

4.3 Color measurements

4.3.1 Reflectance and Color difference ∆E∗ab

The reflectance is obtained by three measurements: reflected intensity of the sample, reflectedintensity of a reference target, and by reflectance data of the same reference target. The lattertwo are for calibration and does not need to be performed for every measurement. The reflectanceof the reference target is measured by the 45/0 reference instrument (X-rite 530 Spectrodensit-ometer). It is important that the sample and reference intensity measurements are made underas similar conditions as possible.

The reference instrument measures reflectance at 31 wavelengths in the interval 400-700 nm.This should be compared to the camera that obtain data at 873 wavelengths in the interval380-800 nm. The natural option is to interpolate the camera data into an identical wavelengthvector like the reference instrument yields.

Since the reference instrument measures at 45/0 geometry and there are no tabulated dataof the reference at other geometries, the demonstration of angular resolved measurements areapproximative.

Measurements of selected color fields from two different test targets (see section 3.1) are comparedwith measurements from the reference instrument. The reflectance is converted into L∗a∗b∗ colorcoordinates and compared in terms of ∆E∗ab.

A value of ∆E∗ab ≈ 2.3 is statistically considered as just noticeable difference, JND. Below thisvalue, color difference is ”unnoticeable” by human visual estimations.

4.3.2 Goniophotometric

Some materials will change color when viewed at different angles. It is because those materialsreflect and absorb different wavelengths of light at different angles. By evaluating samples withthis property (see section 3.1) the capacity of the system to detect these changes is demonstrated.The light source is fixed at 45◦, while the camera angle is changed to 0◦, 15◦, 30◦ and 45◦. In eachof these angles the spectral reflectance and the interpreted color (in RGB) are evaluated.

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5 Results

5.1 Geometric calibration

5.1.1 Region of interest and spatial resolution

In figure 12 in the section: Equipment, an overview of the general setup is shown. The result-ing distances and robot settings of that setup, for different sample widths, are presented intable 1.

Table 1: Measured distances, A (mm), and robot distance settings, B (mm), for different samplewidths. The B settings is specified with tool coordinates that takes the thickness of the samplefixture (17 mm) into account.

Distance/ Sample widthssetting 100 mm 160 mm 210 mmA 230.0 380.0 506.0B 492.0 342.0 217.0

The distance B takes the tool control point in relation to its attachment into account, withspecified tool coordinates. Throughout the thesis it is specified at the surface of the samplefixture, 17 mm in front of the default tool attachment point. An illustration of specified toolcoordinates is shown in figure 24.

Figure 24: Robot control point orientation: default setting vs. specified tool coordinates.

5.1.2 Geometrically correct scans

The following stepping speeds and step lengths presented in table 2 produce geometrically correctscans for the three different sample widths.

Table 2: Calculated, tested and verified stepping speeds and stepping lengths to produce geo-metrically correct scans without oversampling.

Scan method & sample width speed/step lengthContinuous 100 mm 0.813 mm/sContinuous 160 mm 1.272 mm/sContinuous 210 mm 1.672 mm/sStepwise 100 mm 0.072 mmStepwise 160 mm 0.115 mmStepwise 210 mm 0.151 mm

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5.1.3 Sharpness

This evaluation was to get satisfying sharpness in the vertical direction since the camera mustelevate to capture a 2-D image. The results can be viewed in table 3.

Table 3: 10-90% rise distance results for 160 mm sample width.Scan method rise distance down-sampledHorizontal 0.1290Vertical, continuous, without oversampling 0.1509Vertical, continuous, double sample rate 0.1442 0.1346Vertical, continuous, quadruple sample rate 0.1415 0.1321Vertical, step wise, without oversampling 0.1270

These results show that the vertical step wise scans are equally as sharp as the horizontal scanswithout oversampling. The continuous scans are not as sharp as the horizontal, however, over-sampling makes them better.

5.2 Photometric calibration

5.2.1 Light source

In figure 25 the spectral distribution of the illuminant during start-up is shown. The SPD-linefor one hour is lower than for 30 seconds, which indicate that the light source need some time tostabilize.

Figure 25: Spectral distribution during start-up of the light source.

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Figure 26 shows the radiance over time. The signal is stable after 40 minutes, the remainingfluctuation have a relative standard deviation of

σ40−75minx40−75min

≈ 4.63

2.43 · 104≈ 1.91 · 10−4. (16)

Figure 26: Overview of how the light source stabilizes. It remains stable from 40 minutes andforward.

5.2.2 Illumination of sample area

The illumination of the sample area is evaluated and shown in figure 27. It is assessed at fivedifferent heights, evenly spaced over the sample, and at high and low intensity regions.

Figure 27: Spectral distribution of illuminated reference. Measured at five heights in high (left)and low (right) intensity area. Reference heights are 1 = +80 mm (top), 2 = +40 mm, 3 = +-0mm (middle), 4 = -40 mm, 5 = -80 mm (bottom).

There are some small differences in the high intensity area at longer wavelengths while in thelow intensity area, there are almost none.

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5.2.3 Spectral accuracy

The native spectral offset of the system is measured by a He-Ne laser with wavelength 623.8 nmand shown in figure 28. It shows that there is a clear offset of about 13.6 nm which all evaluationsare compensated for.

Figure 28: The native spectral offset of the camera when viewing a 623.8nm laser dot. Left peakis the offset. Right peak is after offset adjustment.

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5.3 Color measurements

5.3.1 Reflectance and Color difference ∆E∗ab

X-rite Color Checker, matte targets

The spectral differences between the reference instrument and the system is shown infigure 29.

Figure 29: The system vs the reference instrument of the X-rite color checker palette.

There are a lot of disturbance in the region with shorter wavelengths as well as a small spectralshift. Other than that the system seems to match the reference instrument quite good.

These reflectance measurements was then converted into L*a*b* color space to be comparedwith the ∆E∗ab method, which gave the following results.

∆E∗ab,yellow= 13

∆E∗ab,red = 11

∆E∗ab,green = 14

∆E∗ab,blue = 4

Again the limits are: ∆E∗ab ≤ 1.0 = JND ”unnoticeable”, 1.0 < ∆E∗ab ≤ 3.0 ”noticeable but notbothersome”, 3.0 < ∆E∗ab ≤ 6.0 ”clearly noticeable and somewhat bothersome” and 6.0 < ∆E∗

”bothersome”. All fall into the fourth boundary except for blue, which falls into the third.

In figure 30 the ∆E∗ab values are illustrated by a ball plot where each ball have its position basedon L*a*b* values provided by the system. The radius of each ball is equal to its ∆E∗ab value,which can be compared to a ball with ∆E∗ab = 1 = JND in the middle.

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Figure 30: Color difference ∆E∗ab results for the X-rite color checker sample. The radius of eachball is its ∆E∗ab value. The small gray ball have the radius of ∆E∗ab = 1 = JND. The L*a*b*coordinates for the balls are: Yellow=(84.3, 7.9, 70.0), Red=(52.3, 47.6, 26.7), Green=(53.7,-28.2, 13.5), Blue=(31.1, -2.4, -48.6).

Kodak Q-60 Color Input Target, glossy targets

The spectral differences between the reference instrument and the system is shown in figure31.

Figure 31: The system vs. the reference instrument of the Kodak Q-60 Color Input Targetpalette.

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Here there are even more disturbance/fluctuations in the green and blue sample. The reflectanceintensities are also overall lower than they were for the X-rite color checker.

The color difference ∆E∗ab for the tested colors of the Kodak Q-60 Color input target are asfollows:

∆E∗ab,yellow= 18

∆E∗ab,red = 30

∆E∗ab,green = 24

∆E∗ab,blue = 16

All these results fall well into the fourth boundary.

In figure 32 the ∆E∗ab values are illustrated by a ball plot where each ball have its position basedon L*a*b* values provided by the system. The radius of each ball is equal to its ∆E∗ab value,which can be compared to a ball with ∆E∗ab = 1 = JND located in origin.

Figure 32: Color difference ∆E∗ab results for the Kodak Q-60 Color Input Target sample. Theradius of each ball is given by its ∆E∗ab value. The small gray ball have the radius of ∆E∗ab =1 = JND. The L*a*b* coordinates for the balls are: Yellow=(77.2, 21.6, 74.7), Red=(43.9, 44.1,20.0), Green=(31.6, -24.9, -0.6), Blue=(22.6, 1.4, -39.5).

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5.3.2 Goniophotometric

Three ChromaFlair samples were run through this process and their respective shifts are green/purple,silver/green and magenta/gold, shown in figures 33, 34 and 35. At the 45/45 geometry, mostlight should reflect off the surface and ”blind” the camera, therefore resulting in white color dueto the calibration. The main reason why the following shifts do not show the ”other” color isbecause a ChromaFlair sample need more extreme viewing angles to get that shift.

Figure 33: Shift in reflectance for the green/purple sample.

Figure 34: Shift in reflectance for the silver/green sample.

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Figure 35: Shift in reflectance for the magenta/gold sample.

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6 Discussion

Photometric calibration

The spectral offset is probably native to the camera and can be observed clearly in the computersoftware SpectralDAQ. It would be interesting to contact the manufacturer to see if there issome kind of firmware update or if they can send a .bnd-file, which is used to set the activebands for the camera, to correct the offset. The offset is at the moment only evaluated for onewavelength and all data is adjusted according to this evaluation. At the moment we don’t knowif this spectral offset change different in other parts of the spectrum.

The distance between the light source and sample can have large impact on the system. A sourcefurther away will illuminate the sample more evenly, therefore reducing the importance of theposition of the reference when calibrating. If it is moved closer, the signal-to-noise ratio willbecome better but it will also increase the demand on calibrations, i.e. reference/calibrationscans in several positions since the lighting will be more uneven.

Color measurements

The main reason for the large color difference results is the large distance between the lightsource and the sample. Since the SPD of the light source is weak for short wavelengths, movingit closer will increase the signal-to-noise ratio, especially in that region. Some signal processingmight also be applied and perhaps consider different interpolation methods.

The reason for higher ∆E∗ab values when evaluating the glossy Kodak sample is because of themajority of incoming light will reflect off in 45◦. Since less light will reflect into the camera thesignal-to-noise ratio is reduced even further, hence larger ∆E∗ab.

The light source was initially positioned at a distance where it illuminated the sample area evenly.However, due to time constraints the impact on the reflectance by moving the light source closerwere not evaluated.

The offset evaluation is not accurate enough. The offset is only evaluated at 623.8 nm andshould be evaluated throughout the whole range. Even after this offset adjustment, there is asmall spectral shift between the system and reference instrument that can be observed in figures29 and 31. This shift most likely the result of the inaccuracy of the reference instrument due toage or low spectral resolution. It is not very accurate in itself, with increments of 10 nm. Forbetter calibration conditions, better reference data is needed.

The goniophotometric demonstration shows that the system can pick up shifts in reflectance thatoccur with changing viewing angle. The most convincing result is from the magenta/gold samplethat seems to go from some kind of dark purple-like color and then shifts into a yellow gold-like.These samples require quite extreme viewing angles to experience the ”complete” shift, probablysomewhere up to the geometry of 45/80-45/85.

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7 Conclusions

The system does not measure colors with satisfying accuracy at the moment. The light sourceneeds to be moved closer to the sample to get better signal-to-noise ratio, especially in the shortwavelength region. However, if this does not improve the results enough, another light sourceshould be considered.

To calibrate the system sufficiently, better reference data is needed.

Since the manual focusing involve a little too much human factors, it should be considered to useonly a single sample-to-camera distance, and take efforts for good focus at that distance beforelocking the lens into place.

”Real” targets samples for sharpness and resolution evaluations should be considered, also tohelp with the focusing.

The system is able to produce images with reflectance values in all pixels. Further the system isable to detect shifts in reflectance when viewing angle is changed. However, it is not calibratedfor this application at the moment.

8 Future work

Contact the camera manufacturer to see if they can help with the spectral offset. Or in otherways, evaluate the whole spectral range of the camera.

Evaluate the light source-to-sample distance.

Consider signal treatments and interpolation methods to reduce noise.

Acquire better reference data, either via better reference instrument or from an institution.

Develop a program to ”connect” the modules. This program should be able to set measurementgeometry and make necessary calibration measurements and thereafter run a corresponding mea-surement sequence.

Measure the BRDF function for actual goniophotometric evaluations.

Develop a sample fixture that allows for both reflectance and transmittance measurements, andto reposition the robot to allow for both kinds of measurements.

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References

[1] Dr. R.W.G. Hunt. ”Measuring Color, third edition,” ISBN 0-86343-387-1, (1998)

[2] Gaurav Sharma. ”Digital Color Imaging Handbook,” ISBN 0-8493-0900-X, (2003)

[3] Niklas Johansson. ”Spectral Goniophotometry,” Applications to Light Scattering in Paper(2013)

[4] Jerker Wagberg. ”optprp - a color properties toolbox”.http://www.mathworks.se/matlabcentral/fileexchange/13788-optprop-a-color-properties-toolbox, (2007), 2014-08-18

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