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

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(a) Depth map (b) Colour map (c) Depth map (d) Colour map Figure 7.6: Shape acquisition test: Human Body. Left column: depth maps; right column: the corresponding textures. 7.3 Correspondences Finding The test framework for evaluate correspondences finding is set as follows. For each object test, we pick two adjacent views, and run the correspondence program on the image pair. Depth and point set data are touched up 208
if there is any obvious errors, before we start finding the correspondence. As introduced earlier, when doing the corner detection the user is provided with a facility to randomise a parameter set, run the program, and the instant results are projected onto the desktop for inspection. The exact value of the parameters such as the search range, the eigenvalue thresh- old or the window size for local aggregation are all hidden from the user. While repeating the process by randomising the parameter set, it is not necessary that the parameter set which yields the most corners is chosen as the optimal value. The user is advised to use his own judgement by looking at the result reflected on the desktop. This is similar to debugging a C program on a local PC, the only difference being that in this application the user doesn’t have to know anything about the technical details which is hidden. Therefore, we apply the same rule of ’how to choose the optimal parameter set’ in the test framework, to simulate the user’s behaviour. 209
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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
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2.2 Active Shape Acquisition Method
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assigned to each pixel to their cor
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(a) Hardware setup. (b) The Digital
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corresponds to the conclusion that
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Figure 2.4: User interacts with the
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tions according to the contents on
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(a) A maze-solving agent tries to f
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mally different objects represents
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Designed with the technology of VAE
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Chapter 3 Calibration 3.1 Introduct
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Calibration principle To calibrate
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section 3.5. Conclusions are given
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3.3 Calibration Parameters 3.3.1 In
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α is the skew coefficient, a scala
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3.3.2 The Reduced Camera Model The
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ordinate, ⎛ ⎜ ⎝ Xc Yc Zc ⎞
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⎛ ⎞ ⎛ 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)
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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
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Although not having a comprehensive
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(a) projector brightness = 0 (b) pr
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Chapter 4 Shape Acquisition 4.1 Int
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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).
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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
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(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
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⎛ ⎞ m ⎜ ⎟ ⎜ ⎟ w ⎜ n
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Figure 4.10: Depth map. 113
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Figure 4.12: Scattered point set in
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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
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To implement the algorithm, we firs
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(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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Page 161 and 162: In figure 6.5 shows the effect of c
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Page 169 and 170: 6.4 Main Utilities In this section
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Page 177 and 178: Figure 6.12: The row index picture
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Page 181 and 182: Figure 6.14: Correspondence Mode: t
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Page 185 and 186: During the course of tuning, the fi
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Page 197 and 198: Thumbnail Object Description Cushio
Page 199 and 200: Object No. of views Initial error (
Page 201 and 202: (a) Depth map (b) Rendered model (c
Page 203 and 204: eing too high that the texture deta
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Page 207: (a) Depth map (front view, before t
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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