Dual Bayesian and Morphology-based Approach for Markerless Human Motion Capture in Natural Interaction Environments
Première édition
Digital watermarking is the art of embedding secret messages in multimedia contents in order to protect their intellectual property. While the watermarking of image, audio and video is reaching maturity, the watermarking of 3D virtual objects is still a technology in its infancy.In this thesis, we focus on two main issues. The first one is the perception of the distortions caused by the watermarking process or by attacks on the surface of a 3D model. The second one concerns the development of techniques able to retrieve a watermark without the availability of the original data and after common manipulations and attacks.Since imperceptibility is a strong requirement, assessing the visual perception of the distortions that a 3D model undergoes in the watermarking pipeline is a key issue. In this thesis, we propose an image-based metric that relies on the comparison of 2D views with a Mutual Information criterion. A psychovisual experiment has validated the results of this metric for the most common watermarking attacks.The other issue this thesis deals with is the blind and robust watermarking of 3D shapes. In this context, three different watermarking schemes are proposed. These schemes differ by the classes of 3D watermarking attacks they are able to resist to. The first scheme is based on the extension of spectral decomposition to 3D models. This approach leads to robustness against imperceptible geometric deformations. The weakness of this technique is mainly related to resampling or cropping attacks. The second scheme extends the first to resampling by making use of the automatic multiscale detection of robust umbilical points. The third scheme then addresses the cropping attack by detecting robust prong feature points to locally embed a watermark in the spatial domain.
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Spécifications
- Éditeur
- Presses universitaires de Louvain
- Partie du titre
-
Numéro 101
- Auteur
- Pedro Correa Hernandez,
- Collection
- Thèses de l'École polytechnique de Louvain
- Langue
- anglais
- Catégorie (éditeur)
- Sciences appliquées > Électricité
- BISAC Subject Heading
- TEC000000 TECHNOLOGY & ENGINEERING
- Code publique Onix
- 06 Professionnel et académique
- CLIL (Version 2013-2019 )
- 3069 TECHNIQUES ET SCIENCES APPLIQUEES
- Date de première publication du titre
- 01 janvier 2006
- Type d'ouvrage
- Thèse
Livre broché
- Date de publication
- 01 janvier 2006
- ISBN-13
- 9782874630293
- Ampleur
- Nombre de pages de contenu principal : 135
- Code interne
- 74041
- Format
- 16 x 24 x 0,8 cm
- Poids
- 393 grammes
- Prix
- 41,00 €
- ONIX XML
- Version 2.1, Version 3
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Sommaire
List of figures
1 Introduction 1
1.1 Context of theWork . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Goal and Motivations . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Targeted Applications . . . . . . . . . . . . . . . . . . . . . . 6
1.4 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Algorithm Overview . . . . . . . . . . . . . . . . . . . . . . . 15
1.5.1 About Silhouette Segmentation . . . . . . . . . . . . . 16
1.6 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Intra-Image Feature Extraction 19
2.1 The Crucial Point Set . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Image Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 Crucial Point Extraction . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 Geodesic Distance Map Computation . . . . . . . . . 23
Geodesic Distances and Geodesic Maps . . . . . . . . 23
Geodesic Maps Computation . . . . . . . . . . . . . . 25
Center of Gravity . . . . . . . . . . . . . . . . . . . . . 26
2.3.2 Geodesic Distance Map Computation Optimization . 28
2.3.3 Analysis of the Geodesic Distance Function . . . . . . 30
2.3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4 Intra-Image Classification . . . . . . . . . . . . . . . . . . . . 41
2.4.1 Morphological Skeletons . . . . . . . . . . . . . . . . . 41
2.4.2 Selective pruning and robust skeletons . . . . . . . . 44
2.4.3 Feature Classification . . . . . . . . . . . . . . . . . . 45
Holes and Loops . . . . . . . . . . . . . . . . . . . . . 53
2.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Morphological Skeletons . . . . . . . . . . . . . . . . . 57
Extraction and Labelling Results . . . . . . . . . . . . 57
2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3 Inter-Image Feature Labelling and Tracking 69
3.1 Crucial Point Extraction . . . . . . . . . . . . . . . . . . . . . 69
3.2 Tracking Step . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.2.1 Mahalanobis Distance and Gating . . . . . . . . . . . 73
3.2.2 Sequencing versus Global Classification Approach . 75
3.3 Detection Step . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.3.1 Prior probability maps . . . . . . . . . . . . . . . . . . 79
3.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.5.1 Synthetic results . . . . . . . . . . . . . . . . . . . . . 88
3.5.2 Real segmentation results . . . . . . . . . . . . . . . . 92
General movement range . . . . . . . . . . . . . . . . 92
Possible application: Virtual aerobic home training . 94
Testing the algorithm flexibility. Application: Virtual Tennis game . . . . . . . . . . . . . . . . 96
Testing the algorithm flexibility: Wheelchair user . . 98
Testing the algorithm robustness limits: Segmentation. Application: Gestural navigation . . . 100
Testing the algorithm robustness limits: Challenging postures . . . . . . . . . . . . . . . . . . . . . 102
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4 Experimenting with possible extensions and perspectives 107
4.1 Stepping into 3D . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.1.1 Triangulation . . . . . . . . . . . . . . . . . . . . . . . 108
4.1.2 Reliability coefficient . . . . . . . . . . . . . . . . . . . 110
4.1.3 3D Tracking . . . . . . . . . . . . . . . . . . . . . . . . 113
4.1.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.2 Taking Crucial Points as input for animation . . . . . . . . . 117
4.2.1 Inverse kinematics . . . . . . . . . . . . . . . . . . . . 117
4.2.2 2D animation model . . . . . . . . . . . . . . . . . . . 119
4.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5 Conclusion 123
5.1 Conclusions and contributions . . . . . . . . . . . . . . . . . 123
5.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.3 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Bibliography 131