Hand Motion from 3D Point Trajectories and a Smooth Surface Model 1st editon by Guillaume Dewaele, Frédéric Devernay, Radu Horaud ISBN 3540219842 9783540219842

Hand Motion from 3D Point Trajectories and a Smooth Surface Model 1st editon by Guillaume Dewaele, Frédéric Devernay, Radu Horaud  – Ebook PDF Instant Download/Delivery. 3540219842, 978-3540219842
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Product details:

ISBN 10:       3540219842
ISBN 13: 978-3540219842
Author:         Guillaume Dewaele, Frédéric Devernay, Radu Horaud
 

A method is proposed to track the full hand motion from 3D points reconstructed using a stereoscopic set of cameras. This approach combines the advantages of methods that use 2D motion (e.g. optical flow), and those that use a 3D reconstruction at each time frame to capture the hand motion. Matching either contours or a 3D reconstruction against a 3D hand model is usually very difficult due to self-occlusions and the locally-cylindrical structure of each phalanx in the model, but our use of 3D point trajectories constrains the motion and overcomes these problems.

Our tracking procedure uses both the 3D point matches between two time frames and a smooth surface model of the hand, build with implicit surface. We used animation techniques to represent faithfully the skin motion, especially near joints. Robustness is obtained by using an EM version of the ICP algorithm for matching points between consecutive frames, and the tracked points are then registered to the surface of the hand model. Results are presented on a stereoscopic sequence of a moving hand, and are evaluated using a side view of the sequence.

 Hand Motion from 3D Point Trajectories and a Smooth Surface Model 1st Table of contents:

1. Introduction

   • 1.1 Background and Motivation
   • 1.2 Importance of Hand Motion Analysis in Computer Vision
   • 1.3 Overview of 3D Point Trajectories in Hand Motion Tracking
   • 1.4 Role of Smooth Surface Models in Enhancing Accuracy
   • 1.5 Objectives and Contributions of the Paper
   • 1.6 Structure of the Paper

2. Related Work

   • 2.1 Hand Motion Analysis and Gesture Recognition
   • 2.2 Tracking Methods for Hand Motion in 3D
   • 2.3 Surface Reconstruction Techniques for Hand Models
   • 2.4 Previous Approaches Using 3D Point Trajectories
   • 2.5 Challenges in Real-Time Hand Tracking and Motion Estimation

3. 3D Point Trajectories and Hand Motion

   • 3.1 Introduction to 3D Point Trajectories
   • 3.2 Representation of Hand Motion Using 3D Points
   • 3.3 Techniques for Extracting 3D Point Trajectories from Video or Sensors
   • 3.4 Motion Estimation from 3D Point Trajectories
   • 3.5 Challenges in Point Cloud Representation and Noise Removal

4. Smooth Surface Model for Hand Motion

   • 4.1 The Concept of Smooth Surface Models
   • 4.2 Surface Reconstruction from 3D Point Data
   • 4.3 Smoothing Techniques for Surface Estimation
   • 4.4 Integration of Surface Models with Hand Motion Tracking
   • 4.5 Application of Geometric Surface Models in Hand Motion Analysis

5. Methodology

   • 5.1 Overview of the Proposed Method for Hand Motion Tracking
   • 5.2 Step 1: Preprocessing and 3D Point Trajectory Extraction
   • 5.3 Step 2: Surface Model Construction from Point Data
   • 5.4 Step 3: Motion Estimation and Analysis Using Surface Models
   • 5.5 Computational Considerations and Optimization
   • 5.6 Handling Noise and Artifacts in Trajectories and Surfaces

6. Experimental Setup and Data Collection

   • 6.1 Dataset Description: 3D Point Trajectories and Motion Data
   • 6.2 Sensor Setup for Capturing Hand Motion (e.g., Motion Capture, RGB-D Cameras)
   • 6.3 Ground Truth for Motion Tracking and Hand Position
   • 6.4 Experimental Protocols and Evaluation Metrics
   • 6.5 Software and Tools Used for Hand Motion Tracking

7. Results and Evaluation

   • 7.1 Evaluation of Tracking Accuracy Using 3D Point Trajectories
   • 7.2 Performance of Smooth Surface Models in Motion Estimation
   • 7.3 Comparison with Existing Methods for Hand Motion Tracking
   • 7.4 Qualitative Results: Visualizations of Hand Motion and Surface Models
   • 7.5 Quantitative Results: Error Analysis and Statistical Metrics

8. Applications and Use Cases

   • 8.1 Hand Gesture Recognition and Human-Computer Interaction
   • 8.2 Virtual Reality and Augmented Reality Applications
   • 8.3 Medical Applications: Hand Rehabilitation and Tracking
   • 8.4 Robotics: Hand Motion in Grasping and Manipulation
   • 8.5 Real-Time Motion Tracking and Feedback Systems

9. Challenges and Future Directions

   • 9.1 Handling Complex Movements and Occlusions in Hand Motion
   • 9.2 Real-Time Implementation and Computational Challenges
   • 9.3 Extending the Model for Multi-Hand or Multi-Object Interactions
   • 9.4 Integration with Other Modalities: Depth, Texture, and Inertial Data
   • 9.5 Potential for Deep Learning in Improving Surface and Motion Estimation

10. Conclusion

    • 10.1 Summary of Key Findings
    • 10.2 Contributions to Hand Motion Tracking Using 3D Point Trajectories and Surface Models
    • 10.3 Practical Implications for Real-World Applications
    • 10.4 Final Remarks and Suggestions for Future Research

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