PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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5.7. Combining MSCC with other local detectors 83 repeatability score [%] 90 80 70 60 50 40 30 MSER Harris-Affine Hessian-Affine IBR MSCC 20 10 0 0 10 20 30 40 50 60 70 80 90 100 viewpoint angle [°] (a) number of correspondences 1400 1200 1000 800 600 400 MSER Harris-Affine Hessian-Affine IBR MSCC 200 0 0 10 20 30 40 50 60 70 80 90 100 viewpoint angle [°] (b) Figure 5.13: (a) Repeatability score for ”Room” scene. (b) Absolute number of correspondences.
5.7. Combining MSCC with other local detectors 84 matchingscore (rel. to #detection) [%] 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 MSER Harris-Affine Hessian-Affine IBR MSCC 0.0 0 10 20 30 40 50 60 70 80 90 100 viewpoint angle [°] (a) matchingscore (rel. to #possible matches) [%] 90 80 70 60 50 40 30 20 10 MSER Harris-Affine Hessian-Affine IBR MSCC 0 0 10 20 30 40 50 60 70 80 90 100 viewpoint angle [°] (b) absolute number of correct matches 400 350 300 250 200 150 100 MSER Harris-Affine Hessian-Affine IBR MSCC 50 0 0 10 20 30 40 50 60 70 80 90 100 viewpoint angle [°] (c) Figure 5.14: (a) Matching score for ”Group” scene relative to number of detections. (b) Matching score for ”Group” scene relative to number of possible matches. (c) Absolute number of correct matches.
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Graz University of Technology Insti
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Abstract Visual map building and lo
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Contents 1 Introduction to mobile r
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CONTENTS vi 7.1.5 Sub-map linking .
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1.1. Localization and map building
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1.3. What has already been achieved
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1.5. How can it get solved? 6 fully
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1.6. Contribution of this thesis 8
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1.7. Structure of the thesis 10 com
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2.2. Localization from point featur
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2.2. Localization from point featur
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2.4. Localization from plane featur
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2.5. Summary 18 or not. Clearly thi
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Chapter 3 Local detectors Research
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3.1. Interest point detectors 22 fu
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3.2. Scale invariant detectors 24 r
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3.2. Scale invariant detectors 26 (
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3.2. Scale invariant detectors 28 3
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3.3. Affine invariant detectors 30
Page 39 and 40: 3.3. Affine invariant detectors 32
Page 47 and 48: 3.4. Comparison of the described me
Page 49 and 50: 3.4. Comparison of the described me
Page 51 and 52: 44 But using a plane to plane homog
Page 53 and 54: 4.2. Representation of the detectio
Page 55 and 56: 4.3. Detection correspondence 48 th
Page 57 and 58: 4.4. Point transfer using the trifo
Page 59 and 60: 4.5. Ground truth generation 52 usi
Page 61 and 62: 4.6. Experimental evaluation 54 Fig
Page 63 and 64: 4.6. Experimental evaluation 56 rep
Page 65 and 66: 4.6. Experimental evaluation 58 MSE
Page 67 and 68: 4.6. Experimental evaluation 60 mat
Page 69 and 70: 4.6. Experimental evaluation 62 vie
Page 71 and 72: 4.6. Experimental evaluation 64 vie
Page 73 and 74: 4.6. Experimental evaluation 66 rel
Page 75 and 76: Chapter 5 Maximally Stable Corner C
Page 77 and 78: 5.1. The MSCC detector 70 (a) (b) (
Page 79 and 80: 5.2. Region representation 72 400 (
Page 81 and 82: 5.3. Computational complexity 74 6.
Page 83 and 84: 5.5. Detection examples 76 paramete
Page 85 and 86: 5.5. Detection examples 78 Figure 5
Page 87 and 88: 5.6. Detector evaluation: Repeatabi
Page 89: 5.7. Combining MSCC with other loca
Page 93 and 94: 5.7. Combining MSCC with other loca
Page 99 and 100: 6.1. Wide-baseline region matching
Page 101 and 102: 6.1. Wide-baseline region matching
Page 103 and 104: 6.2. Piece-wise planar scene recons
Page 117 and 118: Chapter 7 Living in a piecewise pla
Page 119 and 120: 7.1. Map building 112 s,R,t sub-map
Page 121 and 122: 7.1. Map building 114 x = (x 1 ...x
Page 123 and 124: 7.1. Map building 116 distance is u
Page 125 and 126: 7.1. Map building 118 normalization
Page 127 and 128: 7.2. Localization 120 where N = |D
Page 129 and 130: 7.2. Localization 122 registration
Page 131 and 132: 7.2. Localization 124 (a) (b) (c) F
Page 133 and 134: 7.2. Localization 126 3D structure
Page 135 and 136: 7.2. Localization 128 other landmar
Page 137 and 138: Chapter 8 Map building and localiza
Page 139 and 140: 8.2. Map building experiments 132 8
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8.2. Map building experiments 134 D
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8.2. Map building experiments 136 (
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8.2. Map building experiments 138 F
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8.2. Map building experiments 140 F
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8.3. Localization experiments 142 8
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8.3. Localization experiments 144 F
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8.3. Localization experiments 146 F
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8.3. Localization experiments 148 8
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8.3. Localization experiments 150 (
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Chapter 9 Conclusion More than 25 y
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9.1. Future work 154 Map building a
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9.1. Future work 156 information ca
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A.1. Projective ellipse transfer 15
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A.1. Projective ellipse transfer 16
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A.2. Affine approximation of ellips
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Appendix B The trifocal tensor and
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Bibliography [1] S. Atiya and G. Ha
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168 [31] F. Fraundorfer and H. Bisc
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170 [61] U. Köthe. Edge and juncti
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172 [92] F. Schaffalitzky and A. Zi
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PHD Thesis - Institute for Computer Graphics and Vision - Graz ...

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