View Invariant Recognition Using Corresponding Object Fragments 1st edition by Evgeniy Bart, Evgeny Byvatov, Shimon Ullman ISBN 3540219835 9783540219835

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Product details:

ISBN 10:       3540219835
ISBN 13: 978-3540219835
Author:         Evgeniy Bart, Evgeny Byvatov, Shimon Ullman
 

View-Invariant Recognition Using Corresponding Object Fragments 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 The Challenge of View-Invariant Object Recognition
  • 1.3 Corresponding Object Fragments for Robust Recognition
  • 1.4 Objectives and Contributions of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 View-Invariant Recognition Approaches
  • 2.2 Object Fragmentation and Part-Based Recognition
  • 2.3 3D Object Recognition and Pose Estimation
  • 2.4 Techniques for Correspondence Matching
  • 2.5 Limitations and Gaps in Existing Methods

• Mathematical Foundations

  • 3.1 Object Representation and Fragmentation
  • 3.2 View-Invariant Features and Transformations
  • 3.3 Correspondence Matching Algorithms
  • 3.4 Geometric and Photometric Transformations in Object Recognition
  • 3.5 Statistical Models for Fragment Matching

• Object Fragmentation and Correspondence Matching

  • 4.1 Overview of Object Fragmentation Techniques
  • 4.2 Fragment Selection and Feature Extraction
  • 4.3 Correspondence Matching Methods: Geometric and Feature-Based
  • 4.4 Handling Variability in Object Fragments Across Views
  • 4.5 Robustness to Occlusions and Partial Views

• View-Invariant Recognition Framework

  • 5.1 Overview of the Recognition Pipeline
  • 5.2 Learning Invariant Features from Object Fragments
  • 5.3 Multi-View Representation and Correspondence Matching
  • 5.4 Matching Corresponding Fragments Across Views
  • 5.5 Decision Making: Object Classification from Fragment Matches

• Algorithm Design and Implementation

  • 6.1 Overview of the Proposed Algorithm for View-Invariant Recognition
  • 6.2 Data Preprocessing and Fragment Extraction
  • 6.3 Feature Matching and Correspondence Search
  • 6.4 Computational Complexity and Optimization Techniques
  • 6.5 Real-Time Considerations and Efficient Implementation

• Experimental Setup and Evaluation

  • 7.1 Datasets for View-Invariant Object Recognition
  • 7.2 Evaluation Metrics: Recognition Accuracy and Robustness
  • 7.3 Experimental Protocols and Cross-Validation
  • 7.4 Comparison with Existing Recognition Methods
  • 7.5 Performance Evaluation in Challenging Conditions (e.g., Occlusion, Rotation)

• Results and Discussion

  • 8.1 Visual Examples of Fragment-Based Object Recognition
  • 8.2 Quantitative Results: Accuracy and Efficiency of the Recognition System
  • 8.3 Impact of Fragment Matching on Recognition Performance
  • 8.4 Discussion of Challenges in View-Invariant Recognition
  • 8.5 Insights from Experimental Results

• Applications of View-Invariant Recognition

  • 9.1 Robotics and Object Manipulation
  • 9.2 Augmented Reality and Object Tracking
  • 9.3 Surveillance and Security Systems
  • 9.4 Industrial Automation and Quality Control
  • 9.5 Applications in Medical Imaging and Diagnostics

• Challenges and Future Directions

  • 10.1 Handling Complex Object Deformations and Non-Rigid Transformations
  • 10.2 Real-Time Performance for Large-Scale Datasets
  • 10.3 Generalization to Unseen Object Categories
  • 10.4 Integration with Deep Learning Approaches for Fragment Matching
  • 10.5 Future Research Directions in View-Invariant Object Recognition

• Conclusion

  • 11.1 Summary of Key Contributions and Findings
  • 11.2 Practical Implications of Corresponding Object Fragment Recognition
  • 11.3 Limitations and Open Problems
  • 11.4 Final Remarks and Future Work

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