Combining Geometric and View-Based Approaches for Articulated Pose Estimation 1st edition by David Demirdjian ISBN 3540219828 9783540219828

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Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         David Demirdjian 
 

Combining Geometric- and View-Based Approaches for Articulated Pose Estimation 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Challenges in Articulated Pose Estimation
  • 1.3 Geometric vs. View-Based Approaches
  • 1.4 Motivation for Combining Geometric- and View-Based Methods
  • 1.5 Objectives and Contributions of the Paper
  • 1.6 Structure of the Paper

• Related Work

  • 2.1 Overview of Articulated Pose Estimation Techniques
  • 2.2 Geometric Approaches for Pose Estimation
  • 2.3 View-Based Methods for Pose Estimation
  • 2.4 Hybrid Approaches: Combining Geometric and View-Based Techniques
  • 2.5 Limitations of Existing Methods and Gaps in the Literature

• Preliminaries and Background

  • 3.1 Definition of Articulated Pose Estimation
  • 3.2 Geometric Representation of Articulated Objects
  • 3.3 View-Based Representation: Appearance and Projections
  • 3.4 Common Pose Estimation Frameworks and Algorithms
  • 3.5 Coordinate Systems and Transformations in Pose Estimation

• Geometric Approaches for Articulated Pose Estimation

  • 4.1 Kinematic Models and Articulation Constraints
  • 4.2 Inverse Kinematics for Pose Estimation
  • 4.3 Geometric Matching Techniques (e.g., point clouds, 3D meshes)
  • 4.4 Optimization Methods for Geometric Pose Estimation
  • 4.5 Limitations and Assumptions in Geometric-Based Methods

• View-Based Approaches for Articulated Pose Estimation

  • 5.1 2D Pose Estimation from Single and Multiple Views
  • 5.2 Feature Matching and Keypoint Detection
  • 5.3 Depth Estimation and Multi-view Geometry
  • 5.4 Machine Learning and Deep Learning for View-Based Pose Estimation
  • 5.5 Limitations of View-Based Methods: Occlusions and View Dependency

• Combining Geometric- and View-Based Approaches

  • 6.1 Rationale for Combining Geometric and View-Based Methods
  • 6.2 Integration Framework: Geometric Alignment with View-Based Features
  • 6.3 Hybrid Optimization Strategies
  • 6.4 Learning-Based Fusion of Geometric and View-Based Information
  • 6.5 Handling Occlusions and Ambiguities with Combined Approaches

• Algorithm Design and Implementation

  • 7.1 Overview of the Combined Pose Estimation Algorithm
  • 7.2 Step-by-Step Algorithm for Geometric and View-Based Integration
  • 7.3 Handling Multiple Views and Dynamic Changes in Pose
  • 7.4 Real-Time Processing and Scalability Considerations
  • 7.5 Practical Considerations and Computational Efficiency

• Experimental Setup and Evaluation

  • 8.1 Datasets for Articulated Pose Estimation
  • 8.2 Benchmarking Against Other Pose Estimation Methods
  • 8.3 Evaluation Metrics for Pose Accuracy and Robustness
  • 8.4 Cross-validation and Experimental Protocols
  • 8.5 Impact of View and Geometric Information on Estimation Quality

• Results and Discussion

  • 9.1 Quantitative Results of Articulated Pose Estimation
  • 9.2 Performance Comparison of Geometric, View-Based, and Hybrid Methods
  • 9.3 Analysis of Accuracy, Robustness, and Generalization
  • 9.4 Case Studies in Human Pose Estimation and Robotic Applications
  • 9.5 Discussion on Limitations and Trade-offs in Combined Methods

• Applications of Combined Geometric- and View-Based Pose Estimation

  • 10.1 Human Pose Estimation for Computer Vision
  • 10.2 Robotic Manipulation and Motion Planning
  • 10.3 Virtual and Augmented Reality Applications
  • 10.4 Motion Capture Systems in Animation and Gaming
  • 10.5 Medical Imaging and Biomechanics

• Challenges and Future Directions

  • 11.1 Handling Real-Time Performance in Complex Scenarios
  • 11.2 Generalizing to Unseen Poses and Non-Standard Articulations
  • 11.3 Improving Robustness in Low-Quality or Noisy Data
  • 11.4 Integrating Other Sensor Modalities (e.g., depth, IMUs)
  • 11.5 Future Research Directions in Hybrid Pose Estimation Methods

• Conclusion

  • 12.1 Summary of Key Findings and Contributions
  • 12.2 Practical Implications for Articulated Pose Estimation
  • 12.3 Limitations and Opportunities for Improvement
  • 12.4 Final Remarks and Future Work

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