Bias in Shape Estimation 1st edition by Hui Ji, Cornelia Fermuller ISBN 3540219828 9783540219828

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ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:    Hui Ji, Cornelia Fermüller     
 

Bias in Shape Estimation 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Importance of Shape Estimation in Computer Vision and Robotics
  • 1.3 Definition and Impact of Bias in Shape Estimation
  • 1.4 Objectives and Contributions of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Shape Estimation Techniques: A Review
  • 2.2 Types of Bias in Estimation: Statistical and Systematic Bias
  • 2.3 Approaches for Bias Detection and Mitigation in Shape Estimation
  • 2.4 Bias in 3D Reconstruction and Computer Vision
  • 2.5 Existing Solutions and Limitations

• Fundamentals of Shape Estimation

  • 3.1 Overview of Shape Estimation Problems
  • 3.2 Geometric Models and Representation of Shapes
  • 3.3 Methods of Shape Estimation (e.g., stereo vision, depth sensing, photogrammetry)
  • 3.4 Noise and Distortion in Shape Estimation
  • 3.5 Sources of Bias in Shape Estimation

• Types of Bias in Shape Estimation

  • 4.1 Measurement Bias: Sources and Effects
  • 4.2 Model Bias: Parametric Assumptions and Their Consequences
  • 4.3 Systematic Bias vs. Random Bias in Estimation Processes
  • 4.4 Bias Due to Sensor Limitations (e.g., resolution, calibration errors)
  • 4.5 Algorithmic Bias in Shape Reconstruction Techniques

• Quantifying Bias in Shape Estimation

  • 5.1 Mathematical Formulation of Bias in Estimation
  • 5.2 Error Analysis in Shape Estimation
  • 5.3 Statistical Measures for Bias Detection
  • 5.4 Experimental Protocols for Bias Evaluation
  • 5.5 Metrics for Bias in 3D Shapes: Accuracy, Precision, and Robustness

• Mitigating Bias in Shape Estimation

  • 6.1 Preprocessing and Data Calibration to Reduce Bias
  • 6.2 Regularization Techniques for Bias Correction
  • 6.3 Model Refinement and Adaptation to Account for Bias
  • 6.4 Sensor Fusion and Multi-view Methods to Mitigate Bias
  • 6.5 Machine Learning Approaches for Bias Compensation

• Experimental Setup and Evaluation

  • 7.1 Datasets Used for Shape Estimation and Bias Evaluation
  • 7.2 Experimental Setup: Simulation and Real-World Scenarios
  • 7.3 Evaluation Metrics for Shape Estimation Bias
  • 7.4 Comparison of Bias in Different Shape Estimation Methods
  • 7.5 Results of Bias Mitigation Strategies

• Results and Discussion

  • 8.1 Visual and Quantitative Evaluation of Bias in Shape Estimation
  • 8.2 Impact of Bias on Different Shape Estimation Techniques
  • 8.3 Discussion of Bias Mitigation Techniques and Their Effectiveness
  • 8.4 Performance Analysis in Challenging Scenarios (e.g., low-quality sensors, occlusions)
  • 8.5 Limitations and Trade-offs in Bias Mitigation Approaches

• Applications of Shape Estimation with Bias Considerations

  • 9.1 Robotics and Autonomous Navigation
  • 9.2 3D Modeling and Virtual Reality
  • 9.3 Medical Imaging and Shape Reconstruction
  • 9.4 Object Recognition and Tracking
  • 9.5 Industrial Applications: Manufacturing and Quality Control

• Challenges and Future Directions

  • 10.1 Handling Complex and Noisy Data in Shape Estimation
  • 10.2 Real-Time Bias Mitigation for On-the-Fly Shape Estimation
  • 10.3 Generalization of Bias Mitigation Techniques Across Different Domains
  • 10.4 Future Research Directions in Bias Detection and Correction
  • 10.5 Integrating Bias Awareness into Machine Learning Models for Shape Estimation

• Conclusion

  • 11.1 Summary of Key Findings
  • 11.2 Practical Implications for Shape Estimation Systems
  • 11.3 Contributions to the Field of Computer Vision and Robotics
  • 11.4 Final Remarks and Future Work

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