Gesture-Based Interaction with Time-of-Flight Cameras

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Part I Introduction and Time-of-Flight Cameras 1
Page 1 and 2: Aus dem Institut für Neuro- und Bi
Page 3 and 4: Contents Acknowledgements vi I Intr
Page 5 and 6: CONTENTS 7.3.2 Sparse Features . .
Page 7: Acknowledgements There are a number
Page 12 and 13: CHAPTER 1. INTRODUCTION recognition
Page 14 and 15: CHAPTER 1. INTRODUCTION of these fe
Page 16 and 17: CHAPTER 2. TIME-OF-FLIGHT CAMERAS v
Page 18 and 19: CHAPTER 2. TIME-OF-FLIGHT CAMERAS (
Page 20 and 21: CHAPTER 2. TIME-OF-FLIGHT CAMERAS d
Page 22 and 23: CHAPTER 2. TIME-OF-FLIGHT CAMERAS b
Page 24 and 25: CHAPTER 2. TIME-OF-FLIGHT CAMERAS .
Page 30 and 31: CHAPTER 2. TIME-OF-FLIGHT CAMERAS l
Page 32 and 33: CHAPTER 2. TIME-OF-FLIGHT CAMERAS c
Page 34 and 35: CHAPTER 2. TIME-OF-FLIGHT CAMERAS 2
Page 37 and 38: 3 Introduction The second part of t
Page 39 and 40: accuracy of the tracking is around
Page 41 and 42: 4.1 Introduction 4 Shading Constrai
Page 43 and 44: allow arbitrary range maps to be us
Page 45 and 46: additive terms to obtain an energy
Page 47 and 48: 4.2. METHOD Figure 4.1: The intensi
Page 49 and 50: ground truth range map noisy range
Page 51 and 52: 4.3. RESULTS to a standard deviatio
Page 53 and 54: 4.4. DISCUSSION with the statistica
Page 55 and 56: 4.4. DISCUSSION (a) (b) (c) (d) (e)
Page 57 and 58: 5.1 Introduction 5 Segmentation An
Page 59 and 60:
(a) (b) (c) (d) 5.1. INTRODUCTION F
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the mean value µrng(i, j) and are
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1200 900 600 300 0 0 50 100 150 200
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5.1. INTRODUCTION Figure 5.8: An il
Page 68 and 69:
CHAPTER 6. POSE ESTIMATION database
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CHAPTER 6. POSE ESTIMATION Figure 6
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CHAPTER 6. POSE ESTIMATION preserve
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CHAPTER 6. POSE ESTIMATION Figure 6
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CHAPTER 6. POSE ESTIMATION 0.1 0.05
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CHAPTER 6. POSE ESTIMATION but robu
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CHAPTER 7. FEATURES the projection
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CHAPTER 7. FEATURES motion vector f
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CHAPTER 7. FEATURES surface of a fa
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CHAPTER 7. FEATURES ridge ǫ2 saddl
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CHAPTER 7. FEATURES The top plot in
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CHAPTER 7. FEATURES detection rate
Page 92 and 93:
CHAPTER 7. FEATURES 7.2 Scale Invar
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CHAPTER 7. FEATURES 0.5 0.25 0 0.25
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CHAPTER 7. FEATURES As noted in the
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CHAPTER 7. FEATURES ε 0 1.5 1 0.5
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CHAPTER 7. FEATURES range at 35 cm
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CHAPTER 7. FEATURES for non-interac
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CHAPTER 7. FEATURES simple template
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CHAPTER 7. FEATURES tion of Gaussia
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CHAPTER 7. FEATURES In the case of
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CHAPTER 7. FEATURES detection rate
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CHAPTER 7. FEATURES Figure 7.12: Op
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CHAPTER 7. FEATURES false positive
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CHAPTER 7. FEATURES 7.4 Local Range
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CHAPTER 7. FEATURES can only contri
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CHAPTER 7. FEATURES Figure 7.16: Sa
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CHAPTER 7. FEATURES Figure 7.18: Sa
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CHAPTER 7. FEATURES PU RL LR NG PU
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CHAPTER 7. FEATURES 95.83% while th
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8 Introduction In Part II we have i
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9.1 Introduction 9 Facial Feature T
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a) b) 9.2. NOSE TRACKING Figure 9.1
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9.4. CONCLUSIONS Figure 9.2: Exampl
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CHAPTER 10. GESTURE-BASED INTERACTI
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 151 and 152:
11 Generation of Depth of Field Bas
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11.1. INTRODUCTION linear mixture o
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11.2. METHOD is a linear combinatio
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(a) (b) (c) (d) (e) 11.2. METHOD Fi
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11.2. METHOD the optical axis inter
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missing data g ′′ g g ′ 11.2.
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11.3. RESULTS Figure 11.5: Syntheti
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(a) (b) (c) (d) (e) 11.4. DISCUSSIO
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12 Conclusion The results presented
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the traffic. Thus, potential hazard
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BIBLIOGRAPHY Martin Böhme, Martin
Page 174 and 175:
BIBLIOGRAPHY Michael Glodek. Modell
Page 176 and 177:
BIBLIOGRAPHY Timo Kahlmann, Fabio R
Page 178 and 179:
BIBLIOGRAPHY David G. Lowe. Object
Page 180 and 181:
BIBLIOGRAPHY Daniel Potts and Gabri
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BIBLIOGRAPHY Clay Matthew Thompson.
Page 184 and 185:
2-tap pixel, 20 3DV Systems, 8, 9,
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Gesture-Based Interaction with Time-of-Flight Cameras

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