To the Graduate Council: I am submitting herewith a dissertation ...

More documents

Recommendations

Info

Chapter 1: Introduction 5will depend on the distance between the points and on the similarity of their shape andother attributes.Our formulation proved straightforward to implement, especially given the possibilityof using standard gradient-based optimization techniques instead of the specializedheuristic employed by ICP. Even better is the recent availability of a fast computationmethod for our criterion in the form the Fast Gauss Transform [Elgammal03][Yang03].This technique allows for the reduction of computational complexity from square tolinear. By combining these elements and by studying the properties of the Gaussiancriterion we end up with an elegant and practical framework for 3D registration.Quantitative and qualitative analysis will show that our algorithm allows for asignificant increase in the range of convergence as compared with ICP, and hence offersa serious alternative to this standard technique. We will also show that the GaussianFields criterion can be used for multimodal image registration as well. Practicalexamples of 3D modeling, mainly photo-realistic object reconstruction and multi-sensorface modeling, will stress our ultimate objective of automating multimodal scenedescription as well as our application areas. In the process of developing our methodswe also addressed in a novel way the important computer vision problem of Shape fromMotion. In this effort lie the seeds of both the saliency measure and of the registrationapproach. The overall multimodal integration pipeline that focused our work issummarized in Fig. 1.1.1.2 ContributionsIn summary, the most important contributions of this work are the following:• The Gaussian Fields Criterion for Point-Sets Registration
Chapter 1: Introduction 6Visual and Multi-Spectral ImagesRange Maps, 3D Point-SetsShape fromMotion3D-3DRegistrationImage Registration3D-2D RegistrationOverlayMachine or OperatorDecision, action…Segmentation, ObjectRecognitionFig. 1.1. General flowchart of our 3D multimodal modeling system. The shaded boxes are thetasks that were addressed in this dissertation.
Page 2: Multimodal Three Dimensional SceneR
Page 5 and 6: AbstractThe focus of this research
Page 7 and 8: 5.2.3 Limitations of the FGT method
Page 9 and 10: Figures1.1. General flowchart of ou
Page 11 and 12: dataset. ..........................
Page 13 and 14: Chapter 1: Introduction 2The proble
Page 15: Chapter 1: Introduction 4To achieve
Page 19 and 20: Chapter 1: Introduction 8ultimately
Page 21 and 22: Chapter 2: Related Work 10In the li
Page 23 and 24: Chapter 2: Related Work 121. Start
Page 25 and 26: Chapter 2: Related Work 14heuristic
Page 27 and 28: Chapter 2: Related Work 16multi-sen
Page 29 and 30: Chapter 2: Related Work 18To model
Page 31 and 32: Chapter 2: Related Work 20P 1P 2P 3
Page 33 and 34: Chapter 2: Related Work 22A recent
Page 35 and 36: Chapter 3: A Feature Saliency Descr
Page 53 and 54: Chapter 4: Gaussian Fields for Free
Page 67 and 68:
Chapter 4: Gaussian Fields for Free
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Chapter 5: Fast Gauss Transform Met
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Chapter 6: Results 80expression of
Page 93 and 94:
Chapter 6: Results 826.2 3D Synthet
Page 95 and 96:
Chapter 6: Results 84(a)(b)Fig. 6.2
Page 97 and 98:
Chapter 6: Results 86(a)(b)Fig. 6.3
Page 99 and 100:
Chapter 6: Results 88(a)(b)(c)Fig.
Page 101 and 102:
Chapter 6: Results 9025% 32%54% 70%
Page 103 and 104:
Chapter 6: Results 92Z-variance = 2
Page 105 and 106:
Chapter 6: Results 94The scanner ha
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Chapter 6: Results 100Fig. 6.13. Mi
Page 113 and 114:
Chapter 6: Results 102(a)(b)Fig. 6.
Page 115 and 116:
Chapter 6: Results 104Fig. 6.15. Pl
Page 117 and 118:
Chapter 6: Results 106DW50% of Peak
Page 119 and 120:
Chapter 6: Results 1086.3.3 Basins
Page 121 and 122:
Chapter 6: Results 110VanBuildingPa
Page 123 and 124:
Chapter 6: Results 112Fig. 6.21. Co
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Chapter 6: Results 118Fig. 6.27. Ex
Page 131 and 132:
Chapter 6: Results 120We have shown
Page 133 and 134:
Chapter 6: Results 122GearMicrochip
Page 135 and 136:
Chapter 6: Results 124VanBuildingFi
Page 137 and 138:
Chapter 6: Results 126Uniform Sampl
Page 139 and 140:
Chapter 6: Results 128MicrochipVanB
Page 141 and 142:
Chapter 6: Results 130Fig. 6.35 we
Page 143 and 144:
Chapter 6: Results 132(a)(b)(c)(d)F
Page 145 and 146:
Chapter 6: Results 134Using the now
Page 147 and 148:
Chapter 6: Results 1366.4.4 Multimo
Page 149 and 150:
Chapter 7: Conclusions and Future W
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Bibliography 144BIBLIOGRAPHY[Anders
Page 157 and 158:
Bibliography 146[Chiba98] N. Chiba
Page 159 and 160:
Bibliography 148[Fruh01] C. Früh,
Page 161 and 162:
Bibliography 150[Jebara99] T. Jebar
Page 163 and 164:
Bibliography 152IEEE International
Page 165 and 166:
Bibliography 154[Schmid00] C. Schmi
Page 167 and 168:
Bibliography 156[Wollny02] G. Wolln
show all

To the Graduate Council: I am submitting herewith a dissertation ...

Create successful ePaper yourself

Delete template?

Save as template?