Usability of Digital Cameras for Verifying Physically Based ...

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Figure 28: The position of the camera and the color chart with the lower left corner of the front side of the light cabinet as reference point. Figure 29: The angle of the camera was set to 48 ◦ . 48
(a) (b) Figure 30: (a) ColorCheckerSG after correcting for distortion; (b) the subareas of the patches that were used for averaging (blue squares). digital photography and can be solved easily. Software for identifying such pixels is available as freeware (e.g. Dead/Hot Pixel Test [dea06]). Pixels that are labeled as dead or hot pixels are excluded from further calculations. With the third case we have to deal separately, though. We analyzed the deviation of pixel values of several patches, in order to find a method for picking the most representative pixel value. As we cannot pick only one pixel of a patch, we have to take a region of pixels into account. The areas we chose for selecting a representative pixel value can be seen in figure 30 (b). The size of the area depends on the size of the image. For the image seen in figure 30 each area covers 990 (33x30) pixel. We excluded the border of each patch to avoid inaccuracies due to noise and interpolation at the edges. Figure 31 shows histograms of exemplary distributions of pixel values of one channel of a patch. In the first histogram (see figure 31(a)) the values are hardly effected by noise and it is therefore obvious that 229 is a representative value for this kind of distribution. The median (229) and the mean value (228.95) are very close in this case. For the distribution seen in figure 31(b) the representative value is more ambiguous. A considerable amount of pixels has the value 216, which indicates that the value we are searching for lies in between 215 and 216. The median of 215 and the mean value of 215.28 differ noticeably. Here, the mean value seems to better represent the relevance of the particular pixel values. Figure 31(c) shows a distribution were this is not the case, though. We can see that there is a huge amount of noise present and that there are even some severe outliers. These outliers were caused by a light dust particle on a dark patch. The 49
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DISSERTATION Usability of Digital C
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Kurzfassung Physically Based Render
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Acknowledgements I want to thank We
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3 Colorimetry 32 3.1 Color Spaces .
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E.5 Treating the Camera as a Black
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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 and 50: of luminance [GJB80]. In XYZ color
Page 51 and 52: where L ∗ � � Y m = 903.3 Yn
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: Figure 27: The sensor plane marking
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
Page 103 and 104: etter results: One can measure the
Page 105 and 106: Figure 62: A Mathematica generated
Page 107 and 108: 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
Page 113 and 114: in sufficient detail. The approach
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A Radiometric and Photometric Quant
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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.
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E.2 Using an ICC Profile 8 bit jpg
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8 bit jpg 16 bit 62 16.10 16.08 16.
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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
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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
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R G B 31 -38.94 -25.49 29.26 32 -9.
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R G B 93 103.86 109.28 55.06 94 31.
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References [3dS06] 3D Studio Max: w
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[HLR00] Guowei Hong, M. Ronnier Luo
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Color Imaging Conference, pages 250
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[VMKK00] V. Volevich, K. Myszkowski
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