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

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4.6. Experimental evaluation 67 rel. number of non-overlapping regions (cumulative) 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 viewpoint angle (approx.) [°] Harris- Affine Hessian- Affine (a) rel. number of non-overlapping regions (cumulative) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 viewpoint angle (approx.) [°] Harris- Affine Hessian- Affine (b) Figure 4.12: Relative numbers of non-overlapping matched regions for combination of Harris- Affine and Hessian-Affine detector. (a) ”Group” scene (b) ”Room scene”
Chapter 5 Maximally Stable Corner Clusters (MSCC’s) 1 The development of this novel local detector is motivated by the need for highly descriptive and discriminative regions for wide-baseline feature matching. Discriminability means that the detection has a unique appearance and thus it is easy to distinguish from other detections. Descriptiveness means that the detection should possess a significant gray-value variance (e.g. texture) so that a meaningful feature-vector can be built. Both measures play a crucial role in finding correspondences between different images. Thus it is not surprising that already the Moravec operator [79] did account for it. In selecting points which show a high correlation difference if only the image window is shifted a little bit, point locations are detected which are highly descriptive. For instance, image regions with little texture (homogenous) would show a small response while highly textured regions (which usually results in high intensity frequencies) would show a high response. The aim for the new detector was therefore to identify highly textured, thus descriptive regions. The observation that a highly textured image region gives rise to a high number of Harris corners led to the idea to detect distinguished regions based on conglomerations of Harris corners. By means of clustering such regions can be detected. Each detected cluster will represent a distinguished region. The cluster center defines the position of the detection, whereas the outline of the region is defined by the cluster border. Speaking of descriptiveness and discriminability it is interesting to think about the best possible detections. Obviously, the larger an image region the larger the descriptiveness and discriminability. It seems quite convincing that the feature vector characterizing a detection should be calculated from the center of the detection to the image borders itself, thus creating the most discriminating feature vector. However, only small regions are robust against occlusions. Thus, one must be interested in detecting image regions as small as possible with a maximum of descriptiveness. Another important property of a local detector is its repeatability. That means detections are repetitively reported on the same locations although the image is undergoing transformations like rotation, scale change, lighting change, view point change, etc. Such transformations easily occur in practice and a good detector should be invariant to such transformations, or at least robust 1 Based on the publications: F. Fraundorfer, M. Winter, and H. Bischof. Mscc: Maximally stable corner clusters. In Proc. 14th Scandinavian Conference on Image Analysis, Joensuu, Finland, pages 45–54, 2005 [36] F. Fraundorfer, M. Winter, and H. Bischof. Maximally stable corner clusters: A novel distinguished region detector and descriptor. In Proc. 1st Austrian Cognitive Vision Workshop, Zell an der Pram, Austria, pages 59–66, 2005 [37] 68
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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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Page 25 and 26: 2.5. Summary 18 or not. Clearly thi
Page 27 and 28: Chapter 3 Local detectors Research
Page 29 and 30: 3.1. Interest point detectors 22 fu
Page 31 and 32: 3.2. Scale invariant detectors 24 r
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Page 37 and 38: 3.3. Affine invariant detectors 30
Page 47 and 48: 3.4. Comparison of the described me
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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
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Page 73: 4.6. Experimental evaluation 66 rel
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 and 90: 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
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7.1. Map building 118 normalization
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7.2. Localization 120 where N = |D
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7.2. Localization 122 registration
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7.2. Localization 124 (a) (b) (c) F
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7.2. Localization 126 3D structure
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7.2. Localization 128 other landmar
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Chapter 8 Map building and localiza
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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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