Gesture-Based Interaction with Time-of-Flight Cameras

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CONTENTS 4.2.3 Computation of Surface Normals . . . . . . . . . . . . . . . 38 4.2.4 Application to Time-of-Flight Cameras . . . . . . . . . . . . 40 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Synthetic Data . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Real-World Data . . . . . . . . . . . . . . . . . . . . . . . . 44 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5 Segmentation 49 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.1.1 Segmentation Based on a Background Model . . . . . . . . . 50 5.1.2 Histogram-based Segmentation . . . . . . . . . . . . . . . . 53 5.1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 6 Pose Estimation 59 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Qualitative Evaluation . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Quantitative Evaluation of Tracking Accuracy . . . . . . . . . 66 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7 Features 71 iv 7.1 Geometric Invariants . . . . . . . . . . . . . . . . . . . . . . . . . . 75 7.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 75 7.1.2 Geometric Invariants . . . . . . . . . . . . . . . . . . . . . . 76 7.1.3 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.4 Nose Detector . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 7.2 Scale Invariant Features . . . . . . . . . . . . . . . . . . . . . . . . 84 7.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 84 7.2.2 Nonequispaced Fast Fourier Transform (NFFT) . . . . . . . . 85 7.2.3 Nose Detection . . . . . . . . . . . . . . . . . . . . . . . . . 88 7.2.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 90 7.2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.3 Multimodal Sparse Features for Object Detection . . . . . . . . . . . 95 7.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 95
CONTENTS 7.3.2 Sparse Features . . . . . . . . . . . . . . . . . . . . . . . . . 96 7.3.3 Nose Detection . . . . . . . . . . . . . . . . . . . . . . . . . 98 7.3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 7.3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 7.4 Local Range Flow for Human Gesture Recognition . . . . . . . . . . 108 7.4.1 Range Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 7.4.2 Human Gesture Recognition . . . . . . . . . . . . . . . . . . 111 7.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 7.4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 III Applications 119 8 Introduction 121 9 Facial Feature Tracking 123 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 9.2 Nose Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 9.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 9.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 10 Gesture-Based Interaction 129 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 10.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 10.2.1 Pointing Gesture . . . . . . . . . . . . . . . . . . . . . . . . 131 10.2.2 Thumbs-up Gesture . . . . . . . . . . . . . . . . . . . . . . 135 10.2.3 System Calibration . . . . . . . . . . . . . . . . . . . . . . . 136 10.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 10.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 11 Depth of Field Based on Range Maps 143 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 11.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 11.2.1 Upsampling the TOF range map . . . . . . . . . . . . . . . . 146 11.2.2 Preprocessing the Image Data . . . . . . . . . . . . . . . . . 148 11.2.3 Synthesis of Depth of Field . . . . . . . . . . . . . . . . . . . 150 11.2.4 Dealing with Missing Data . . . . . . . . . . . . . . . . . . . 152 11.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 v
Page 1 and 2: Aus dem Institut für Neuro- und Bi
Page 3: Contents Acknowledgements vi I Intr
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
Page 35: Part II Algorithms 27
Page 38 and 39: CHAPTER 3. INTRODUCTION straint: If
Page 40 and 41: CHAPTER 3. INTRODUCTION the class i
Page 42 and 43: CHAPTER 4. SHADING CONSTRAINT Besid
Page 44 and 45: CHAPTER 4. SHADING CONSTRAINT an in
Page 46 and 47: CHAPTER 4. SHADING CONSTRAINT 4.2.3
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CHAPTER 4. SHADING CONSTRAINT inten
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CHAPTER 4. SHADING CONSTRAINT sures
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CHAPTER 5. SEGMENTATION (a) (b) Fig
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CHAPTER 5. SEGMENTATION (a) (b) (c)
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CHAPTER 5. SEGMENTATION (a) (b) Fig
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CHAPTER 5. SEGMENTATION Figure 5.7:
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6.1 Introduction 6 Pose Estimation
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6.2. METHOD resolve disambiguities
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6.2. METHOD Figure 6.2: Graph model
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6.3. RESULTS Figure 6.3: Point clou
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6.3. RESULTS exactly which part of
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6.4. DISCUSSION learning. As a resu
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7 Features In image processing, the
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The first type of feature is relate
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7.1 Geometric Invariants 7.1.1 Intr
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7.1. GEOMETRIC INVARIANTS The featu
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4 3 ǫ2 2 5 ×10−3 1 7.1. GEOMETR
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7.1. GEOMETRIC INVARIANTS Figure 7.
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7.1. GEOMETRIC INVARIANTS We were a
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7.2. SCALE INVARIANT FEATURES measu
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an algorithm for the fast evaluatio
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7.2. SCALE INVARIANT FEATURES the o
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7.2. SCALE INVARIANT FEATURES exhib
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detection rate detection rate 1 0.8
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7.2. MULTIMODAL SPARSE FEATURES 7.3
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7.3. MULTIMODAL SPARSE FEATURES Fig
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7.3. MULTIMODAL SPARSE FEATURES Fig
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7.3. MULTIMODAL SPARSE FEATURES eac
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7.3. MULTIMODAL SPARSE FEATURES tem
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false positive rate 0.4 0.35 0.3 0.
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7.3. MULTIMODAL SPARSE FEATURES thi
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7.4. LOCAL RANGE FLOW FOR HUMAN GES
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Part III Applications 119
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CHAPTER 8. INTRODUCTION an alternat
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CHAPTER 9. FACIAL FEATURE TRACKING
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CHAPTER 9. FACIAL FEATURE TRACKING
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10.1 Introduction 10 Gesture-Based
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10.2. METHOD scenarios: One, where
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10.2. METHOD Figure 10.3: Segmented
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Table 10.1: Interpretation of point
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corner along the ray. 10.2. METHOD
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600 500 400 300 200 100 0 0 10 20 3
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10.4. DISCUSSION tention here was t
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CHAPTER 11. DEPTH OF FIELD BASED ON
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CHAPTER 12. CONCLUSION alization of
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Bibliography ARTTS 3D TOF Database.
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BIBLIOGRAPHY James R. Diebel and Se
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BIBLIOGRAPHY ence on Computer Visio
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BIBLIOGRAPHY Stefan Kunis and Danie
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BIBLIOGRAPHY Thierry Oggier, Michae
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BIBLIOGRAPHY Rudolf Schwarte, Horst
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BIBLIOGRAPHY ’06: Proceedings of
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modulation frequency, 8, 23, 26 Mon
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Gesture-Based Interaction with Time-of-Flight Cameras

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