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

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Figure 1.1: Mixed Reality. this way, objects can appear to be augmented [52, 73], or the user can ma- nipulate graphical data by gesture [63, 13, 30]. A significant property of such systems is that 3D objects and projected images are combined in a single mixed environment. 1.3 Goals We contend that in many non-interactive vision problems, a valid and sometimes superior solution can be attained through a human user or users collaborating with automated analysis. Previous work at York has reported applications in fast panorama construction [80], AudioPhotoDesk [41], d-touch [29], movie footage logging [70] and this thesis considers 3D 22
object acquisition – in a human-computer collaborative way. The impor- tance of user in the VAE presented in this thesis is highlighted, as it enables 3D modelling without expensive presentation systems (e.g. servos etc.). Any such system must combine vision and interaction techniques with the design goal of higher efficiency than a purely automated system that requires passive human operator time. The one presented in this thesis uses the projector-camera pair of a VAE to acquire range images, then user interaction in the augmented environment to identify correspond- ing points for building a full 3D model. The automation can then take over again to suggest prototype registrations for further adjustment by the user. There are also simple facilities for touching up range images. The result is an efficient 3D acquisition system that can be deployed without conventional input devices such as keyboard, mouse, or laser pointers. In short, this work aims: • To analyse and extend the use of video as an input device. • To devise and implement different image and video processing com- ponents that make an augmented reality 3D input device possible. • To design a human-computer collaborative system for inputting 3D shapes, and evaluate its performance. 23
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: the power of computer, in a visual
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 ⎜ ⎟ ⎜ ⎜ ⎟
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)
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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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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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