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

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7 System Evaluation 195 7.1 Test Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 7.1.1 An Overview . . . . . . . . . . . . . . . . . . . . . . . 196 7.1.2 Object Descriptions . . . . . . . . . . . . . . . . . . . . 196 7.2 Shape Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . 198 7.2.1 The Owl Experiment . . . . . . . . . . . . . . . . . . . 200 7.2.2 The Football and Stand Experiment . . . . . . . . . . . 200 7.2.3 The Cushion and Human Body Experiment . . . . . . . 206 7.3 Correspondences Finding . . . . . . . . . . . . . . . . . . . . 208 7.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 8 Conclusions 213 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 8.2 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 8.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 A Declarations for class CButton 230 B Declarations for class CPointSet 236 C Declarations for class CView 240 10
List of Figures 1.1 Mixed Reality. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1 Optical flow of approaching objects. . . . . . . . . . . . . . . 31 2.2 The DigitalDesk. (image courtesy of the Computer Labora- tory, University of Cambridge) . . . . . . . . . . . . . . . . . 37 2.3 An image of the BrightBoard. (image courtesy of the Com- puter Laboratory, University of Cambridge) . . . . . . . . . . 39 2.4 User interacts with the ALIVE system. (image courtesy of the MIT Media Lab) . . . . . . . . . . . . . . . . . . . . . . . . 41 2.5 The LivePaper system in use. (image courtesy of the Visual Systems Lab, University of York) . . . . . . . . . . . . . . . . 42 2.6 The LivePaper applications. (image courtesy7 of the Visual Systems Lab, University of York) . . . . . . . . . . . . . . . . 43 2.7 Snapshots of Penpets in action. (image courtesy of the Visual Systems Lab, University of York) . . . . . . . . . . . . . . . . 45 2.8 Audio d-touch interface (the augmented musical stave). (image courtesy of the Computer Laboratory, University of Cam- bridge) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 11
Page 1 and 2: Human-Computer Collaboration in Vid
Page 3 and 4: Abstract The role of the computer h
Page 5 and 6: Ipswich, I have collaborated with m
Page 7 and 8: 2.3 Video Augmented Environments (V
Page 9: 5.4 Fusion . . . . . . . . . . . .
Page 13 and 14: 4.5 Stripes being projected onto a
Page 15 and 16: 6.17 Correspondence Mode: correlate
Page 17 and 18: List of Acronyms AD Absolute Intens
Page 19 and 20: Chapter 1 Introduction 1.1 Problem
Page 21 and 22: the power of computer, in a visual
Page 23 and 24: object acquisition - in a human-com
Page 25 and 26: Chapter 8 draws the conclusions and
Page 27 and 28: Although the system proposed here c
Page 29 and 30: 2.1 Image based 3D capture methods
Page 31 and 32: 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 ⎜ ⎟ ⎜ ⎜ ⎟
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garded as in world coordinate space
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manually it is very hard to hold th
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(a) blue and cyan mixed pattern (b)
Page 73 and 74:
3.4.5 Camera Calibration An automat
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dimensional distortion vector is us
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Cancel out the scale factor w by di
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⎛ ⎞ ⎛ ⎞ Xw ⎜ ⎟ xc ⎜
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Freedom (DOF), a minimum of 4 point
Page 83 and 84:
Although not having a comprehensive
Page 85 and 86:
(a) projector brightness = 0 (b) pr
Page 87 and 88:
Chapter 4 Shape Acquisition 4.1 Int
Page 89 and 90:
application and the generation of t
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served image is fixed for every pix
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methods have their own strengths, h
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e discussed later in section 4.4).
Page 97 and 98:
Row Decimal (Binary) 0 767 (1011111
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Figure 4.4: Binary encoded pattern
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By iterating this approach across t
Page 103 and 104:
(a) level = 5. (b) level = 10. (ali
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(a) Before the subtraction. (b) Aft
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y the lighting conditions. Figure 4
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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
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contribution. In section 4.4.2 a me
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Chapter 5 Registration of Point Set
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ut also that this is what humans ar
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5.2 Background Figure 5.1: A routin
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and di (equation 5.2) to the origin
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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:
Page 135 and 136:
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
Page 147 and 148:
sults by adjusting the parameter se
Page 149 and 150:
data are visualised. 2. A planar su
Page 151 and 152:
6.2 Widgets Provided for Interactio
Page 153 and 154:
(a) A projected image. (b) The obse
Page 155 and 156:
of a user’s fingertips, and locat
Page 157 and 158:
classifications by modelling them a
Page 159 and 160:
in lighting conditions such as sunr
Page 161 and 162:
In figure 6.5 shows the effect of c
Page 163 and 164:
(a) The projected buttons. (b) The
Page 165 and 166:
lob in the resultant foreground reg
Page 167 and 168:
Figure 6.8: A screen shot of the wo
Page 169 and 170:
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
Page 175 and 176:
Figure 6.10 shows the projected dis
Page 177 and 178:
Figure 6.12: The row index picture
Page 179 and 180:
6.4.4 Mode 3: Correspondence In Cor
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Figure 6.14: Correspondence Mode: t
Page 183 and 184:
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
Page 189 and 190:
and translation vectors from view B
Page 191 and 192:
and itself. By fusing view 4 into t
Page 193 and 194:
6.5 Conclusions In this chapter we
Page 195 and 196:
Chapter 7 System Evaluation Most of
Page 197 and 198:
Thumbnail Object Description Cushio
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Object No. of views Initial error (
Page 201 and 202:
(a) Depth map (b) Rendered model (c
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eing too high that the texture deta
Page 205 and 206:
(a) Depth map (b) Colour map (c) De
Page 207 and 208:
(a) Depth map (front view, before t
Page 209 and 210:
if there is any obvious errors, bef
Page 211 and 212:
objects. The correspondence search
Page 213 and 214:
Chapter 8 Conclusions 8.1 Summary A
Page 215 and 216:
configuration of the projector-came
Page 217 and 218:
As mentioned in section 6.5.1, robu
Page 219 and 220:
[7] S.S. Beauchemin and J.L. Barron
Page 221 and 222:
[26] R. Cipolla, T. Drummond, and D
Page 223 and 224:
[45] J. Guehring. Dense 3d surface
Page 225 and 226:
[65] B.D. Lucas and T. Kanade. An i
Page 227 and 228:
[84] K. Sato and S. Inokuchi. Three
Page 229 and 230:
[105] G. Wiora. High-resolution mea
Page 231 and 232:
28 10, 428, // 7: thumbnail (80 x 6
Page 233 and 234:
124 CTRL_ROI_VSHRINK, 125 CTRL_ROI_
Page 235 and 236:
214 void Flash(); 215 void Highligh
Page 237 and 238:
31 CvScalar *mpColour_bk; 32 33 //-
Page 239 and 240:
118 //-----------------------------
Page 241 and 242:
31 CvRect mDepthRect, mTextRect, mM
Page 243:
114 115 //-------------------------
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