Multiscale Inverse Compositional Alignment for Subdivision Surface Maps 1st editon by Igor Guskov ISBN 3540219842 9783540219842

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Product details:

ISBN 10:       3540219842
ISBN 13: 978-3540219842
Author:       Igor Guskov  
 

 Multiscale Inverse Compositional Alignment for Subdivision Surface Maps 1st Table of contents:

1. Introduction

   • 1.1 Background and Motivation
   • 1.2 Subdivision Surfaces and Their Role in Computer Graphics
   • 1.3 Inverse Compositional Alignment: Concept and Significance
   • 1.4 Multiscale Approaches for Alignment Tasks
   • 1.5 Contributions and Scope of the Paper
   • 1.6 Structure of the Paper

2. Related Work

   • 2.1 Overview of Subdivision Surfaces and Their Applications
   • 2.2 Existing Alignment Techniques in Computer Vision and Graphics
   • 2.3 Inverse Compositional Algorithms in Image and Surface Alignment
   • 2.4 Multiscale Methods in Geometric Alignment
   • 2.5 Limitations of Current Methods and Need for Improved Techniques

3. Subdivision Surfaces and Their Mathematical Foundations

   • 3.1 Introduction to Subdivision Surfaces
   • 3.2 Refinement Schemes: Catmull-Clark and Other Variants
   • 3.3 Mathematical Representation of Subdivision Surfaces
   • 3.4 Properties of Subdivision Surfaces for Geometric Modeling
   • 3.5 Challenges in Aligning Subdivision Surfaces

4. Inverse Compositional Alignment Algorithm

   • 4.1 Overview of the Inverse Compositional Approach
   • 4.2 Derivation of the Inverse Compositional Algorithm
   • 4.3 Optimization Criteria and Cost Functions
   • 4.4 Benefits of Inverse Compositional Methods in Alignment
   • 4.5 Comparison with Other Alignment Techniques (e.g., Direct Composition, Gradient Descent)

5. Multiscale Approaches for Alignment

   • 5.1 The Concept of Multiscale Representation
   • 5.2 Why Multiscale Methods Improve Alignment Efficiency and Accuracy
   • 5.3 Multiscale Image and Surface Pyramid Construction
   • 5.4 Pyramid-Based Alignment for Subdivision Surfaces
   • 5.5 Integration of Multiscale Inverse Compositional Alignment

6. Multiscale Inverse Compositional Alignment for Subdivision Surface Maps

   • 6.1 Overview of the Proposed Method
   • 6.2 Step 1: Surface Map Representation and Preprocessing
   • 6.3 Step 2: Alignment at Coarse Scales
   • 6.4 Step 3: Refinement at Finer Scales
   • 6.5 Step 4: Final Surface Alignment and Post-Processing
   • 6.6 Handling Non-Rigid Deformations and Transformations
   • 6.7 Computational Complexity and Performance Considerations

7. Experimental Setup and Evaluation

   • 7.1 Datasets and Surface Map Types Used for Evaluation
   • 7.2 Setup for Alignment Experiments
   • 7.3 Evaluation Metrics for Alignment Accuracy and Speed
   • 7.4 Comparative Analysis: Multiscale vs. Traditional Alignment
   • 7.5 Sensitivity Analysis of Parameters and Scalability

8. Results and Discussion

   • 8.1 Visual Results of Multiscale Alignment on Subdivision Surfaces
   • 8.2 Quantitative Evaluation: Alignment Accuracy and Convergence
   • 8.3 Comparative Analysis with Other Alignment Algorithms
   • 8.4 Impact of Multiscale Methods on Speed and Robustness
   • 8.5 Case Studies and Real-World Applications

9. Applications

   • 9.1 3D Shape Matching and Alignment in Computer Graphics
   • 9.2 Animation and Morphing of Subdivision Surfaces
   • 9.3 Medical Imaging and Surface Registration
   • 9.4 Reverse Engineering and 3D Reconstruction
   • 9.5 Augmented Reality and Virtual Reality

10. Challenges and Future Directions

    • 10.1 Handling Complex Geometries and Large-Scale Data
    • 10.2 Improving Robustness Against Noise and Missing Data
    • 10.3 Real-Time Performance and Implementation in Interactive Systems
    • 10.4 Extending Multiscale Inverse Compositional Alignment to Non-Rigid Surfaces
    • 10.5 Future Directions in Multiscale Alignment Algorithms

11. Conclusion

    • 11.1 Summary of Contributions
    • 11.2 Practical Implications of Multiscale Inverse Compositional Alignment
    • 11.3 Concluding Remarks and Future Work
12.  

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