Region Based Segmentation on Evolving Surfaces with Application to 3D Reconstruction of Shape and Piecewise Constant Radiance 1st edition by Hailin Jin, Anthony Yezzi, Stefano Soatto ISBN 3540219835 9783540219835

Region-Based Segmentation on Evolving Surfaces with Application to 3D Reconstruction of Shape and Piecewise Constant Radiance 1st edition by Hailin Jin, Anthony J. Yezzi, Stefano Soatto- Ebook PDF Instant Download/Delivery. 3540219835, 978-3540219835
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ISBN 10:       3540219835
ISBN 13: 978-3540219835
Author:         Hailin Jin, Anthony J. Yezzi, Stefano Soatto
 

Region-Based Segmentation on Evolving Surfaces with Application to 3D Reconstruction of Shape and Piecewise Constant Radiance 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Challenges in 3D Reconstruction and Surface Segmentation
  • 1.3 Importance of Piecewise Constant Radiance in Reconstruction
  • 1.4 Objectives and Contributions of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Overview of 3D Surface Reconstruction Techniques
  • 2.2 Region-Based Segmentation Approaches in Computer Vision
  • 2.3 Methods for Modeling Piecewise Constant Radiance
  • 2.4 Surface Evolution Models and Their Applications
  • 2.5 Limitations of Existing Methods and Open Challenges

• Mathematical Foundations

  • 3.1 Representation of 3D Surfaces and Meshes
  • 3.2 Geometric Properties of Evolving Surfaces
  • 3.3 Region-Based Segmentation: Definitions and Metrics
  • 3.4 Piecewise Constant Radiance: Models and Assumptions
  • 3.5 Mathematical Framework for Surface Evolution and Segmentation

• Region-Based Segmentation on Evolving Surfaces

  • 4.1 Overview of Region-Based Segmentation Techniques
  • 4.2 Segmentation Criteria on Dynamic Surfaces
  • 4.3 Evolution of Surfaces and its Impact on Segmentation
  • 4.4 Algorithms for Region Growing and Surface Partitioning
  • 4.5 Handling Topological Changes and Singularities during Evolution

• 3D Reconstruction of Shape

  • 5.1 Overview of Shape Reconstruction from Surface Data
  • 5.2 Surface Reconstruction Algorithms: Marching Cubes, Delaunay Triangulation
  • 5.3 Using Segmentation to Enhance Shape Reconstruction
  • 5.4 Integrating Region-Based Segmentation with 3D Reconstruction
  • 5.5 Case Studies of 3D Shape Reconstruction Using Evolving Surfaces

• Modeling Piecewise Constant Radiance

  • 6.1 Concept of Piecewise Constant Radiance in 3D Environments
  • 6.2 Radiance Estimation from Surface Segments
  • 6.3 Radiometric Processing Techniques for Surface Segmentation
  • 6.4 Applications in Lighting and Appearance Modeling
  • 6.5 Handling Variations in Illumination and Surface Properties

• Algorithm Design and Implementation

  • 7.1 Overview of the Proposed Segmentation and Reconstruction Framework
  • 7.2 Input Data: Surface Geometry and Radiance Information
  • 7.3 Region-Based Segmentation Algorithm Design
  • 7.4 Surface Evolution and Reconstruction Pipeline
  • 7.5 Computational Complexity and Optimization Considerations

• Experimental Setup and Evaluation

  • 8.1 Datasets and Test Cases for 3D Reconstruction and Segmentation
  • 8.2 Evaluation Metrics: Accuracy of Segmentation and Reconstruction
  • 8.3 Performance Evaluation: Segmentation Quality and Reconstruction Speed
  • 8.4 Comparison with Traditional 3D Reconstruction Methods
  • 8.5 Results from Synthetic and Real-World Data

• Results and Discussion

  • 9.1 Visual Examples of Segmentation and Reconstruction Results
  • 9.2 Accuracy of Shape Reconstruction with Region-Based Segmentation
  • 9.3 Impact of Piecewise Constant Radiance Modeling on Reconstruction
  • 9.4 Discussion of Error Analysis and Limitations
  • 9.5 Insights and Observations from Experimental Results

• Applications of Region-Based Segmentation and 3D Reconstruction

  • 10.1 Applications in Computer Vision and Object Recognition
  • 10.2 Medical Imaging: Reconstruction of Anatomical Structures
  • 10.3 Robotics and Path Planning using 3D Shape Reconstruction
  • 10.4 Virtual Reality and Augmented Reality Applications
  • 10.5 Applications in Cultural Heritage and Archaeology

• Challenges and Future Directions

  • 11.1 Robustness to Noise and Missing Data in Real-World Datasets
  • 11.2 Handling Complex Surface Topologies and Deformations
  • 11.3 Real-Time Processing for Dynamic Environments
  • 11.4 Integration with Deep Learning for Improved Segmentation and Radiance Estimation
  • 11.5 Future Research Directions in Surface Evolution and 3D Reconstruction

• Conclusion

  • 12.1 Summary of Key Contributions and Findings
  • 12.2 Practical Implications of Region-Based Segmentation in 3D Reconstruction
  • 12.3 Limitations of Current Work and Areas for Improvement
  • 12.4 Closing Remarks and Future Work

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