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

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LivePaper A recent system [52] by Robinson and Robertson provides a VAE in which individual sheets of pages, cards and books are placed on an instru- mented tabletop to activate their enhancement. It appears to the user as if the paper has additional properties with new visual and auditory features. Figure 2.5: The LivePaper system in use. (image courtesy of the Visual Systems Lab, University of York) A sheet of paper is detected through boundary extraction in an ob- served image, and then the projector displays the associated augmenta- 42
tions according to the contents on the current page recognised by the system. The augmented video signals remain projected onto the page. An interactive menu is provided beside the page to provide finger-triggered functionality. A number of sample applications have been developed to illustrate the feasibility of the LivePaper system. These applications include an architectural visualisation tool (figure 2.6(a)) which projects a 3D hidden-line rendering of walls onto a page, page sharing, remote collaboration (figure 2.6(b)), and World Wide Web (WWW) page viewing. From the user’s per- spective, all of these applications are attributes of the particular page, not features of the tabletop. (a) The architectural visualisation application. (b) The collaborative drawing application. Figure 2.6: The LivePaper applications. (image courtesy7 of the Visual Sys- tems Lab, University of York) Another application of LivePaper is an audio player. When a page such 43
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 and 10: 5.4 Fusion . . . . . . . . . . . .
Page 11 and 12: List of Figures 1.1 Mixed Reality.
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: Figure 2.4: User interacts with the
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 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
Page 91 and 92: served image is fixed for every pix
Page 93 and 94:
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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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
Page 229 and 230:
[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 //-----------------------------
Page 241 and 242:
31 CvRect mDepthRect, mTextRect, mM
Page 243:
114 115 //-------------------------
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