Gesture-Based Interaction with Time-of-Flight Cameras

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CHAPTER 2. TIME-OF-FLIGHT CAMERAS .2-tap pixel: .4-tap pixel: .1 st .2 nd .s(t) . .A0 .A1 .A2 .A3 .A0 .A1 .A2 .A3 .A0 .A2 .A0 .A2 .A1 .A3 .A1 .A3 Figure 2.5: Illustration of the measurement sequence for the samples A0, . . . A3 for both the 4-tap pixel and the 2-tap pixel. while increasing the signal-to-noise ratio of the measurement. While the 4-tap pixel resembles a straightforward implementation of the measurement principle introduced in Section 2.4 it has a major disadvantage. The four potential wells for storing the photoelectrons take up a large portion of the total space of one pixel. Thus, the actual photo-sensitive area of a pixel is very small, i.e. the 4- tap pixel has a low so-called fill factor. As a result either the size of a pixel has to be very large or the exposure time of the camera has to be increased. Both approaches have their drawbacks. Large pixels reduce the effective spatial resolution of the camera. At the same time, the extension of a pixel is limited because the photoelectrons travel to the potential wells by diffusion and the expected amount of time for this diffusion process increases quadratically with the mean distance the photoelectrons have to travel before reaching the potential wells. Longer exposure times reduce the temporal resolution and also introduce so-called motion artefacts, which we will dis- cuss in more detail at a later point. Instead of implementing the 4-tap pixel, most recent cameras rely on the so- called 2-tap pixel. Instead of having four potential wells for storing the photoelectrons during the estimation of A0, . . . , A3, the samples are estimated in two consec- utive steps: First, the sensor integrates over two intervals of the periodic signal to estimate A0 and A2. For this operation only two potential wells are required; hence, 20 .t
2.6. MEASUREMENT ACCURACY the name 2-tap pixel. After A0 and A2 have been estimated over a number of periods of the modulation signal, the two values are read out and stored in an internal regis- ter of the camera. Once the readout is completed, the sensor shifts to measuring A1 and A3. In principle, the camera performs two independent measurements and the distance can only be computed after these two measurements have been completed. This approach is also depicted in Figure 2.5. A main drawback of the 2-tap implementation originates from the fact that the sensor makes two independent measurements at different points in time to obtain one range estimate in a pixel. Imagine that an object boundary moves across the pixel in the moment the sensor switches between the two measurements. In that case, A0 and A2 correspond to a different phase shift than A1 and A3. Thus, both the computation of φ and the range value are erroneous. Furthermore, we integrate over a time window ∆t = Tmod 2 to estimate A0, . . . , A3 in the 2-tap pixel. This results in a lower demodulation contrast of cdemod = 2 π ≈ 0.64 in comparison to cdemod ≈ 0.90 for the 4-tap pixel. Although the 4-tap pixel seems to be the design of choice from this point of view, the 2-tap pixel has the major advantage that the fill factor is increased significantly. At the same time, the pixel is easily scalable, i.e. the potential wells are located on opposite sides of the pixel and the distance between the containers can be set to the optimal value. Here, the optimal value refers to a tradeoff between a low mean distance for the photoelectrons to travel to the potential wells and a large area of the light sensitive area. The scala- bility results from the fact that the pixels can be extended easily along the axis that is perpendicular to the potential wells to further increase the light-sensitive area of the pixel. This is the reason why the majority of TOF cameras are based on the 2-tap pixel. 2.6 Measurement Accuracy The accuracy of the TOF range measurement depends mainly the signal-to-noise ratio of the samples A0, . . . , A3. The signal-to-noise ratio is influenced by two different factors. The first is related to the conditions of the background illumination, i.e. the intensity of the ambient light illuminating the scene. The second depends on the actual process of generating the emitted signal e(t) by the active illumination unit. The influence of the ambient light can be reduced to a large extent through the use of a bandpass filter tuned to the wave length of the infrared light of the active illumination and a circuit that actively suppresses the contributions of the ambient 21
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
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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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