pigmented colorants: dependence on media and time - Cornell ...

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The Munsell color system, a particular implementation of HSV , is based on these notions of color perception and is widely used in art as well as many other industries. This color space is defined by a physical collection of samples. Neigh- boring color swatches in the model are said to be perceptually equidistant from one another, based on averages of numerous studies involving jnd’s. The system is a cylindrical coordinate system: a color wheel plus an irregular 2D grid around the core. The notation for a given color is specified as HV/C (hue, value/chroma). Hue is characterized by 100 equally spaced hues around the perimeter. Value varies along the vertical axis from black (0) to white (10). Chroma, increases from no saturation (0) on the center line outward to full saturation (10 to 18, depending on the hue). Figure 3.26: Cutaway of the Munsell color solid and the corresponding constant hue page, 5Y. Adapted from [JF01]. There are many reasons for the popularity and usefulness of the Munsell system. It is exemplified by real color chips in the Munsell Book of Color [Mun], which is a carefully controlled illustration of the system; it is an open-ended system in the chroma dimension so that new, vivid <strong>colorants</strong> and new sources of color can be accommodated; and has been defined in terms of the 1931 CIE illuminant C, 81
enabling one to transform between the two spaces. Instead of physical collections of samples, a number of attempts have been made define a perceptually uniform color space mathematically. The most notable are the L ∗ u ∗ v ∗ and L ∗ a ∗ b ∗ color spaces, both nonlinear mathematical transformations from XY Z space. Each space is designed with respect to a reference white color (Xn,Yn,Zn), usually one of the CIE standard illuminants, scaled such that Yn = 100. Both spaces use the same definition of L ∗ : L ∗ ⎧ 1 ⎪⎨ Y 3 116 Yn = ⎪⎩ Y 903.3 Yn − 16 , Y Yn ≥ .008856 , otherwise 82 (3.9) Note that L ∗ = 100 for the reference white when Y = Yn. In fact, L ∗ may be used to consider the lightness of the color. For the L ∗ u ∗ v ∗ color space, the other components for the conversion between XY Z and L ∗ u ∗ v ∗ are given by: where u ∗ =13L ∗ (u ′ − u ′ n) v ∗ =13L ∗ (u ′ − u ′ n) (3.10) u ′ 4X = X +15Y +3Z v ′ 9Y = X +15Y +3Z u ′ n = v ′ n = 4Xn X +15Y +3Z 9Yn X +15Y +3Z
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PIGMENTED COLORANTS: DEPENDENCE ON
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ABSTRACT We present a physically ba
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Acknowledgements This work would no
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Table of Contents 1 Introduction 1
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List of Tables 2.1 Average pigment
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3.15 Photoreceptor absorption .....
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B.15 Variation of spectral reflecta
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corrode the surfaces of materials.
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Figure 1.1: Detail of the Azor-Sado
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However, since mural artwork is typ
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at a very high quality (albeit limi
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heterogeneous sum of very different
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which linseed oil derives (the most
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oration of a rigidly painted layer
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ing will hit the ground and reflect
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tion of paint tubes in 1841 and the
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Figure 2.8: Natural pigments of ant
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ecome more common recently. The Ame
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A V = O(r2 ) O(r3 ) = 1 O(r) 24 (2.
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Table 2.2: Pigment specific gravity
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provide the underlying color to a p
Page 43 and 44: though the speed of light in air is
Page 45 and 46: In general, when light travels from
Page 47 and 48: materials while lit from behind. Li
Page 49 and 50: Table 2.4: Index of refraction η f
Page 51 and 52: to completely congeal. Hence, these
Page 53 and 54: ond together to form proteins. Exam
Page 55 and 56: to its original state by any means.
Page 57 and 58: While it has been used since the 19
Page 59 and 60: organic binder found in more conven
Page 61 and 62: 3.1.1 Visible Light Spectrum Light
Page 63 and 64: Figure 3.2: Hue, brightness and sat
Page 65 and 66: measures how much light is reflecte
Page 67 and 68: Figure 3.6: Additive color mixing o
Page 69 and 70: Figure 3.9: The physical amount of
Page 71 and 72: The vascular tunic lies on the wall
Page 73 and 74: Figure 3.11: Cross section of the r
Page 75 and 76: Not all areas have the same sensiti
Page 77 and 78: identify and distinguish between va
Page 79 and 80: that range between zero and full in
Page 81 and 82: spectrum. This insures that the res
Page 83 and 84: Figure 3.19: Graphical representati
Page 85 and 86: X x = (X + Y + Z) Y y = (X + Y + Z)
Page 87 and 88: color into the appropriate RGB colo
Page 89 and 90: components in the image (the smalle
Page 91 and 92: haviors of subtractive mixing and i
Page 93: of perceptually equal sizes. Unfort
Page 97 and 98: andwidth as converted signals are s
Page 99 and 100: Figure 3.28: Retinal ganglion recep
Page 101 and 102: example shown in Figure 3.28. Under
Page 103 and 104: Figure 3.31: The work of Josef Albe
Page 105 and 106: sensations for each trial and picke
Page 107 and 108: Chapter 4 Previous Work There is no
Page 109 and 110: different location. Subsurface scat
Page 111 and 112: describes the scattering and absorp
Page 113 and 114: only accounts for a small percentag
Page 115 and 116: σ ′ t = σa + σ ′ s and α
Page 117 and 118: 104 Figure 4.4: A model of an alaba
Page 119 and 120: effects or are too computationally
Page 121 and 122: 108 Figure 4.6: Results using Kubel
Page 123 and 124: specifying pigments works adequatel
Page 125 and 126: color representation, it is not fea
Page 127 and 128: time. A metallic surface patina is
Page 129 and 130: the system consisted of wetness and
Page 131 and 132: or darkening enables the museum per
Page 133 and 134: in front of a displayed work. Felle
Page 135 and 136: light exposure. This effect is espe
Page 137 and 138: over time. Perceptually, these hues
Page 139 and 140: The rate of change of the concentra
Page 141 and 142: over white grounds) suffer the grea
Page 143 and 144: protects the deeper remaining color
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light source. This method is used w
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chemical smog and have been identif
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Chapter 5 Preparation & Measurement
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tending transparent pigments used i
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140 Figure 5.1: Spectral reflectanc
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the requirements of the work at han
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144 Figure 5.2: Left: Magnified vie
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Table 5.2: Relevant data regarding
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historically one of the best grades
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created far too strong of a binder,
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too brittle and cracking. While wor
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154 Figure 5.4: Reflectance values
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5.1.5 Painting After the layers of
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The gray card reflected 18% of the
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160 Figure 5.8: The optical setup f
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Note that harmonics reflect at the
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Note that the calibration factors f
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Chapter 6 Experimental Results 6.1
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168 Figure 6.1: Lapis Lazuli in dif
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(such as CIE standards C, D50, orD6
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172 Table 6.2: Perceptual differenc
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174 Figure 6.3: Munsell plots of La
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and j has a saturation of red rsat,
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178 Figure 6.4: Variation of spectr
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visible spectrum are increasing at
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182 Figure 6.5: Munsell plots of La
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trying to match color ca from the p
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as it ages. This is in direct contr
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teractive viewer. In our applicatio
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190 Figure 7.1: Our simulated canva
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where n is the normal at the curren
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In summary, our implementation prov
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Chapter 8 Conclusion We have studie
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inks are beginning to find their wa
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Appendix A Derivation of K-M theory
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∆i − =(K + S) idx 202 ∆j −
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Equation A.8 is now: Rearranging yi
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and if the paint is infinitely thic
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One concatenates the layers into a
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210 Figure B.1: Cold Glauconite in
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Table B.1: Color conversions from t
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214 Figure B.4: Munsell plots of Co
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216 Figure B.5: Burnt Sienna in dif
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220 Figure B.8: Munsell plots of La
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222 Figure B.9: Red Ochre Tint in d
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226 Figure B.12: Munsell plots of C
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228 Figure B.13: Variation of spect
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230 Figure B.15: Variation of spect
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Table B.13: Color conversions of Bu
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234 Figure B.18: Munsell plots of C
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236 Figure B.20: Munsell plots of R
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References [Alb71] Josef Albers. In
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240 [GLL + 04] Michael Goesele, Hen
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242 [KM31] Paul Kubelka and Franz M
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[Rat72] Floyd Ratliff. Contour and
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