Gesture-Based Interaction with Time-of-Flight Cameras

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3 Introduction The second part of this thesis is devoted to computer vision algorithms designed for TOF camera data. In this context, we will demonstrate the advantage of a combined image sensor that delivers both range and intensity, i.e. we will explicitly show how the combination of both types of data significantly improves the performance of algorithms in contrast to using either data alone. The first section of this chapter will focus on the improvement of the range measurement by exploiting the intensity image under the well-defined lighting conditions provided by the TOF camera illumi- nation. Secondly, we will address the topic of image segmentation. Here, the goal is to identify connected image regions that depict an object or a person present in the scene. These results will then be used in an algorithm for the estimation of human pose that fits a simple model of the human body in 3D. Finally, we will turn to the discussion of suitable image features for encoding relevant properties of TOF images. In this context, we will first discuss geometrically motivated features that are related to the Gaussian curvature. We will then reformulate the computation of these features in 3D to achieve scale invariance. A third type of features is obtained using the sparse coding principle. These three types of features will be evaluated in the context of detecting facial features, such as the nose. Finally, we will turn to the computation of range flow and will use the resulting 3D motion vectors for the recog- nition of human gestures. In the following, we will give a brief overview of the four topics discussed in the scope of Part II of thesis within Chapter 4 through Chapter 7. Shading Constraint Improves TOF Measurements In Chapter 4, we describe a technique for improving the accuracy of range maps mea- sured by a TOF camera. Our technique is based on the observation that the range map and intensity image are not independent but are linked by the shading con- 29
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 11 and 12: 1 Introduction Nowadays, computers
Page 13 and 14: the scene (typically with light nea
Page 15 and 16: 2.1 Introduction 2 Time-of-Flight C
Page 17 and 18: 2.2. STATE-OF-THE-ART TOF SENSORS r
Page 19 and 20: 2.3. ALTERNATIVE OPTICAL RANGE IMAG
Page 21 and 22: 2.3. ALTERNATIVE OPTICAL RANGE IMAG
Page 23 and 24: 2.4 Measurement Principle 2.4. MEAS
Page 25 and 26: .I(t) .A .B . .A0 .A1 .A2 .A3 2.5.
Page 27 and 28: 2.5. TECHNICAL REALIZATION form H(f
Page 29 and 30: 2.6. MEASUREMENT ACCURACY the name
Page 31 and 32: 2.7 Limitations 2.7. LIMITATIONS As
Page 33 and 34: 2.7. LIMITATIONS objects than the i
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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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Page 80 and 81: CHAPTER 7. FEATURES the projection
Page 82 and 83: CHAPTER 7. FEATURES motion vector f
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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
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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
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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
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BIBLIOGRAPHY Michael Glodek. Modell
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BIBLIOGRAPHY Timo Kahlmann, Fabio R
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BIBLIOGRAPHY David G. Lowe. Object
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BIBLIOGRAPHY Daniel Potts and Gabri
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BIBLIOGRAPHY Clay Matthew Thompson.
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2-tap pixel, 20 3DV Systems, 8, 9,
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Gesture-Based Interaction with Time-of-Flight Cameras

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