Model Selection for Range Segmentation of Curved Objects 1st editon by Alireza Bab Hadiashar, Niloofar Gheissari ISBN 3540219842 9783540219842

 Model Selection for Range Segmentation of Curved Objects 1st editon by Alireza Bab Hadiashar, Niloofar Gheissari  – Ebook PDF Instant Download/Delivery. 3540219842, 978-3540219842
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ISBN 10:       3540219842
ISBN 13: 978-3540219842
Author:         Alireza Bab Hadiashar, Niloofar Gheissari
 

 Model Selection for Range Segmentation of Curved Objects 1st Table of contents:

1. Introduction

   • 1.1 Background and Motivation
   • 1.2 Importance of Range Segmentation in 3D Object Recognition
   • 1.3 Challenges in Segmenting Curved Objects from Range Data
   • 1.4 Objectives and Contributions of the Paper
   • 1.5 Structure of the Paper

2. Related Work

   • 2.1 Overview of Range Data Segmentation Techniques
   • 2.2 Segmentation Approaches for Curved Objects
   • 2.3 Model Selection and Its Role in Range Segmentation
   • 2.4 Machine Learning Methods for Segmentation
   • 2.5 Summary of Existing Techniques and Their Limitations

3. Theoretical Background

   • 3.1 Range Data and 3D Point Cloud Representation
   • 3.2 Geometrical Properties of Curved Objects
   • 3.3 Model-Based Segmentation of 3D Data
   • 3.4 Types of Models for Segmenting Curved Surfaces
   • 3.5 Criteria for Effective Model Selection in Range Segmentation

4. Model Selection Criteria for Range Segmentation

   • 4.1 General Approach to Model Selection in Segmentation
   • 4.2 Evaluating Fit Quality: Error Metrics and Fitting Criteria
   • 4.3 Handling Noise and Incomplete Data in Model Selection
   • 4.4 Trade-offs in Model Complexity and Accuracy
   • 4.5 Robustness and Generalization of Segmentation Models
   • 4.6 Adaptive Model Selection Techniques

5. Methodology for Range Segmentation of Curved Objects

   • 5.1 Overview of the Proposed Segmentation Framework
   • 5.2 Step 1: Preprocessing Range Data for Segmentation
   • 5.3 Step 2: Model Fitting and Selection
   • 5.4 Step 3: Segmentation of Curved Objects Using Chosen Models
   • 5.5 Step 4: Post-Processing and Refinement of Segmentation
   • 5.6 Integration with Other Segmentation Techniques (e.g., Edge Detection, Clustering)

6. Algorithms for Model-Based Segmentation

   • 6.1 Model Fitting Algorithms for Curved Objects (e.g., RANSAC, Least Squares)
   • 6.2 Surface Reconstruction Algorithms from Range Data
   • 6.3 Optimization Techniques for Model Selection
   • 6.4 Evaluation of Segmentation Performance: Quantitative Metrics
   • 6.5 Comparison of Different Model Selection Strategies

7. Experimental Setup and Data Collection

   • 7.1 Description of Datasets Used for Range Segmentation
   • 7.2 Data Preprocessing: Noise Removal and Normal Estimation
   • 7.3 Experimental Setup for Model Selection and Segmentation
   • 7.4 Evaluation Metrics for Segmentation Accuracy and Efficiency
   • 7.5 Benchmarking Against Other Segmentation Methods

8. Results and Discussion

   • 8.1 Visual Results of Range Segmentation on Curved Objects
   • 8.2 Quantitative Evaluation of Model Selection and Segmentation Accuracy
   • 8.3 Impact of Different Models on Segmentation Performance
   • 8.4 Comparative Analysis with Other Segmentation Techniques
   • 8.5 Robustness and Scalability of the Model Selection Approach
   • 8.6 Application to Real-World Curved Object Segmentation

9. Applications

   • 9.1 Applications in 3D Object Recognition and Classification
   • 9.2 Industrial Applications: Quality Control and Inspection
   • 9.3 Medical Imaging: Segmentation of Curved Anatomical Structures
   • 9.4 Cultural Heritage Preservation and 3D Digitization
   • 9.5 Robotics and Automation: Grasping and Object Manipulation

10. Challenges and Future Directions

    • 10.1 Handling Highly Complex Curved Objects and Large Datasets
    • 10.2 Improving Segmentation Robustness Under Noisy Conditions
    • 10.3 Real-Time Model Selection for Dynamic Range Data
    • 10.4 Incorporating Learning-Based Approaches for Model Selection
    • 10.5 Future Research on Adaptive and Hybrid Segmentation Models

11. Conclusion

    • 11.1 Summary of Contributions and Findings
    • 11.2 Practical Implications for Range Segmentation of Curved Objects
    • 11.3 Closing Remarks and Future Work

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