Human-Computer Collaboration in Video-Augmented ... - Index of

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A similar problem is also encountered in chapter 4, where a plain white image is projected onto the interface to illuminate the object being mea- sured for the camera to take an image as the colour map. In day time where sufficient ambient light is available, the image can be taken without any illumination from the projector. However in the evening when lights off, projector illumination is essential while capturing an image of the object because it is the only light source. Furthermore, ambient light being too strong will affect the optimal projection affects the projection as well because it can over-illuminate the scene and weaken the projection signals. Therefore, choosing a universal brightness level of projector illumination for all the aforementioned scenarios can be problematic. 84
(a) projector brightness = 0 (b) projector brightness = 128 (c) red pixel values of (a) (d) red pixel values of (b) Figure 3.9: Pixel values of an image captured from a plain desktop. (bottom two showing the red channel only) Figure 3.9 shows an example of different projector illuminations. The top two images are the image captured when the projection brightness is 0 and 128 respectively. The bottom two are the corresponding distribution of the pixel values across the planar surface (only red channels are shown, while the green and blue channels have similar distributions). The average pixel values in (d) is higher and (b) as expected. In both images a slope is noticed because the top of the desktop is closer to the window hence more 85
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Human-Computer Collaboration in Vid
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Abstract The role of the computer h
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Ipswich, I have collaborated with m
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2.3 Video Augmented Environments (V
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5.4 Fusion . . . . . . . . . . . .
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List of Figures 1.1 Mixed Reality.
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4.5 Stripes being projected onto a
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6.17 Correspondence Mode: correlate
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List of Acronyms AD Absolute Intens
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Chapter 1 Introduction 1.1 Problem
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the power of computer, in a visual
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object acquisition - in a human-com
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Chapter 8 draws the conclusions and
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Although the system proposed here c
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2.1 Image based 3D capture methods
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Comprehensive reviews of the aforem
Page 33 and 34: 2.2 Active Shape Acquisition Method
Page 35 and 36: assigned to each pixel to their cor
Page 37 and 38: (a) Hardware setup. (b) The Digital
Page 39 and 40: corresponds to the conclusion that
Page 41 and 42: Figure 2.4: User interacts with the
Page 43 and 44: tions according to the contents on
Page 45 and 46: (a) A maze-solving agent tries to f
Page 47 and 48: mally different objects represents
Page 49 and 50: Designed with the technology of VAE
Page 51 and 52: Chapter 3 Calibration 3.1 Introduct
Page 53 and 54: Calibration principle To calibrate
Page 55 and 56: section 3.5. Conclusions are given
Page 57 and 58: 3.3 Calibration Parameters 3.3.1 In
Page 59 and 60: α is the skew coefficient, a scala
Page 61 and 62: 3.3.2 The Reduced Camera Model The
Page 63 and 64: ordinate, ⎛ ⎜ ⎝ Xc Yc Zc ⎞
Page 65 and 66: ⎛ ⎞ ⎛ x ⎜ ⎟ ⎜ ⎜ ⎟
Page 67 and 68: garded as in world coordinate space
Page 69 and 70: manually it is very hard to hold th
Page 71 and 72: (a) blue and cyan mixed pattern (b)
Page 73 and 74: 3.4.5 Camera Calibration An automat
Page 75 and 76: dimensional distortion vector is us
Page 77 and 78: Cancel out the scale factor w by di
Page 79 and 80: ⎛ ⎞ ⎛ ⎞ Xw ⎜ ⎟ xc ⎜
Page 81 and 82: Freedom (DOF), a minimum of 4 point
Page 83: Although not having a comprehensive
Page 87 and 88: Chapter 4 Shape Acquisition 4.1 Int
Page 89 and 90: application and the generation of t
Page 91 and 92: served image is fixed for every pix
Page 93 and 94: methods have their own strengths, h
Page 95 and 96: e discussed later in section 4.4).
Page 97 and 98: Row Decimal (Binary) 0 767 (1011111
Page 99 and 100: Figure 4.4: Binary encoded pattern
Page 101 and 102: By iterating this approach across t
Page 103 and 104: (a) level = 5. (b) level = 10. (ali
Page 105 and 106: (a) Before the subtraction. (b) Aft
Page 107 and 108: y the lighting conditions. Figure 4
Page 109 and 110: This is inspired by one of the prop
Page 111 and 112: ⎛ ⎞ m ⎜ ⎟ ⎜ ⎟ w ⎜ n
Page 113 and 114: Figure 4.10: Depth map. 113
Page 115 and 116: Figure 4.12: Scattered point set in
Page 117 and 118: perimental results suggest these pr
Page 119 and 120: contribution. In section 4.4.2 a me
Page 121 and 122: Chapter 5 Registration of Point Set
Page 123 and 124: ut also that this is what humans ar
Page 125 and 126: 5.2 Background Figure 5.1: A routin
Page 127 and 128: and di (equation 5.2) to the origin
Page 129 and 130: Dy = ⎡ ⎤ −1 ⎢ 0 ⎣ −1 0
Page 131 and 132: To implement the algorithm, we firs
Page 133 and 134: (a) W = 64 (b) W = 256 Figure 5.4:
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form between them, as a result step
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Figure 5.7: Data structure of a poi
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Bigger and fewer voxels gives coars
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(a) vase (horizontal shot) (b) poin
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Figure 5.13: Manual tuning of point
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5.6 Conclusions In this chapter a f
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sults by adjusting the parameter se
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data are visualised. 2. A planar su
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6.2 Widgets Provided for Interactio
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(a) A projected image. (b) The obse
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of a user’s fingertips, and locat
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classifications by modelling them a
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in lighting conditions such as sunr
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In figure 6.5 shows the effect of c
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(a) The projected buttons. (b) The
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lob in the resultant foreground reg
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Figure 6.8: A screen shot of the wo
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6.4 Main Utilities In this section
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Figure 6.9: Screen shot of the syst
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In Inspect Mode, the user adjusts t
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Figure 6.10 shows the projected dis
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Figure 6.12: The row index picture
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6.4.4 Mode 3: Correspondence In Cor
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Figure 6.14: Correspondence Mode: t
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Figure 6.16: Correspondence Mode: e
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During the course of tuning, the fi
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panel area represented by the resis
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and translation vectors from view B
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and itself. By fusing view 4 into t
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6.5 Conclusions In this chapter we
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Chapter 7 System Evaluation Most of
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Thumbnail Object Description Cushio
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Object No. of views Initial error (
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(a) Depth map (b) Rendered model (c
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eing too high that the texture deta
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(a) Depth map (b) Colour map (c) De
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(a) Depth map (front view, before t
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if there is any obvious errors, bef
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objects. The correspondence search
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Chapter 8 Conclusions 8.1 Summary A
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configuration of the projector-came
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As mentioned in section 6.5.1, robu
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[7] S.S. Beauchemin and J.L. Barron
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[26] R. Cipolla, T. Drummond, and D
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[45] J. Guehring. Dense 3d surface
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[65] B.D. Lucas and T. Kanade. An i
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[84] K. Sato and S. Inokuchi. Three
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[105] G. Wiora. High-resolution mea
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28 10, 428, // 7: thumbnail (80 x 6
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124 CTRL_ROI_VSHRINK, 125 CTRL_ROI_
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214 void Flash(); 215 void Highligh
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31 CvScalar *mpColour_bk; 32 33 //-
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118 //-----------------------------
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31 CvRect mDepthRect, mTextRect, mM
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114 115 //-------------------------
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