Usability of Digital Cameras for Verifying Physically Based ...

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Figure 20: The CIE 1931 chromaticity diagram showing the wavelengths of light and the xy coordinates [cie06b]. L ∗ � Y = 116 a ∗ = 500 b ∗ = 200 Yn � � X � 1 3 − 16 Xn � � Y Yn � 1 3 � Y − Yn � 1 3 � Z − Zn � � 1 3 where Xn, Yn and Zn are the tristimulus values of the reference white. For small values of V , V ∈ {X, Y, Z}, the normal formulae are replaced by the following Vn modified formulae: 34 � 1 3 �
where L ∗ � � Y m = 903.3 Yn a ∗ � � � � �� X Y m = 500 f − f Xn Yn b ∗ � � � � �� Y Z m = 200 f − f Yn � � � � 1 X X 3 f = Xn Xn � � � � X X f = 7.787 Xn Yn Xn � � � � 1 Y Y 3 f = Yn Yn � � � � Y Y f = 7.787 Yn � 1 3 � � � Z Z f = Zn Zn � � � � Z Z f = 7.787 Zn Zn 3.1.3 CIE L ∗ u ∗ v ∗ Color Space Zn + 16 116 + 16 116 + 16 116 for Y Yn for X Xn for X Xn for Y Yn for Y Yn for Z Zn for Z Zn ≤ 0.008856 > 0.008856 ≤ 0.008856 > 0.008856 ≤ 0.008856 > 0.008856 ≤ 0.008856. CIE L ∗ u ∗ v ∗ color space is similar to the L ∗ a ∗ b ∗ color space as it is also a non- linear transformation of the CIE XYZ color space and approximately perceptually uniform. They have the same lightness axis L ∗ , but the other two components are computed differently, as can be seen in the following formulae: 35
Page 1 and 2: DISSERTATION Usability of Digital C
Page 3 and 4: Kurzfassung Physically Based Render
Page 5 and 6: Acknowledgements I want to thank We
Page 7 and 8: 3 Colorimetry 32 3.1 Color Spaces .
Page 9 and 10: E.5 Treating the Camera as a Black
Page 11 and 12: 20 The CIE 1931 chromaticity diagra
Page 13 and 14: 53 (a) Density plot for using the c
Page 15 and 16: List of Tables 1 Camera settings th
Page 17 and 18: 1 Introduction In recent years, a l
Page 19 and 20: 1.3 Applications of Physically Base
Page 21 and 22: matter are calculated in XYZ space.
Page 23 and 24: Figure 2: A photograph of the real
Page 25 and 26: (a) Photo (b) Default (c) 2 Bounces
Page 27 and 28: y a light source on top of the cube
Page 29 and 30: Figure 7: Points A to F were select
Page 31 and 32: Figure 9: A difference image of pho
Page 33 and 34: Figure 12: From top to bottom: a hi
Page 35 and 36: (a) Figure 13: (a) Left side: measu
Page 37 and 38: Figure 14: Six photocells were posi
Page 39 and 40: Figure 16: The left image shows a r
Page 41 and 42: (a) (b) Figure 18: (a) The validati
Page 43 and 44: shape of the caustic. 2.3 Analytica
Page 45 and 46: two test cases, but instead of the
Page 47 and 48: segments of the sphere and its actu
Page 49: of luminance [GJB80]. In XYZ color
Page 53 and 54: (a) (b) Figure 21: Chromaticity dia
Page 55 and 56: 4 Digital Cameras Pattanaik et al.
Page 57 and 58: Figure 23: The RAW image data is co
Page 59 and 60: 5 Physically Based Rendering System
Page 61 and 62: see section 7.4. By avoiding XYZ sp
Page 63 and 64: Figure 27: The sensor plane marking
Page 65 and 66: (a) (b) Figure 30: (a) ColorChecker
Page 67 and 68: available on MAC systems. We chose
Page 69 and 70: (a) (b) (c) Figure 34: Standard dev
Page 71 and 72: Figure 35: Eighty measurement spots
Page 73 and 74: (a) (b) (c) (d) Figure 37: (a) Lumi
Page 75 and 76: (a) (b) Figure 40: (a) Luminance di
Page 77 and 78: i \ j 0 1 2 3 4 5 6 7 8 9 10 11 12
Page 79 and 80: (a) (b) (c) (d) Figure 43: (a) Lumi
Page 81 and 82: (a) (b) Figure 46: Standard deviati
Page 83 and 84: (a) (b) Figure 49: (a) The measurem
Page 85 and 86: of each patch using a spectrophotom
Page 87 and 88: Figure 51: L ∗ a ∗ b ∗ differ
Page 89 and 90: (a) (b) Figure 53: (a) Density plot
Page 91 and 92: (a) (b) (c) Figure 55: Histograms o
Page 93 and 94: Using an ICC profile obviously impr
Page 95 and 96: (a) (b) (c) (d) Figure 58: Histogra
Page 97 and 98: 5 6 7 8 9 10 11 Min 0.50 0.49 0.13
Page 99 and 100: in the light transport simulation o
Page 101 and 102:
the internal data of a camera and w
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etter results: One can measure the
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Figure 62: A Mathematica generated
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their internal color space. This mi
Page 109 and 110:
Figure 66: Surface reflectance valu
Page 111 and 112:
as bad compared to other methods. S
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in sufficient detail. The approach
Page 115 and 116:
A Radiometric and Photometric Quant
Page 117 and 118:
B.6 Goniophotometer A goniophotomet
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C.3 Color Chart Figure 68: A Konica
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D Web Resources of Validation Data
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8 bit jpg 16 bit 29 11.58 11.52 11.
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8 bit jpg 16 bit 91 17.21 17.11 17.
Page 127 and 128:
E.2 Using an ICC Profile 8 bit jpg
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8 bit jpg 16 bit 62 16.10 16.08 16.
Page 131 and 132:
8 bit jpg 16 bit 124 11.08 10.89 11
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1 2 3 4 31 9.10 3.61 4.26 5.96 32 8
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1 2 3 4 93 6.25 4.11 4.05 8.69 94 4
Page 137 and 138:
E.4 Adaptive Mapping - Higher Order
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5 6 7 8 9 10 11 62 3.42 2.52 2.51 2
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5 6 7 8 9 10 11 124 2.10 2.14 2.96
Page 143 and 144:
R G B 31 -38.94 -25.49 29.26 32 -9.
Page 145 and 146:
R G B 93 103.86 109.28 55.06 94 31.
Page 147 and 148:
References [3dS06] 3D Studio Max: w
Page 149 and 150:
[HLR00] Guowei Hong, M. Ronnier Luo
Page 151 and 152:
Color Imaging Conference, pages 250
Page 153:
[VMKK00] V. Volevich, K. Myszkowski
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