Intrinsic Images by Entropy Minimization 1st edition by Graham Finlayson, Mark Drew, Cheng Lu ISBN 3540219828 9783540219828

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Product details:

ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Graham D. Finlayson, Mark S. Drew, Cheng Lu
 

Intrinsic Images by Entropy Minimization 1st Table of contents:

1. Introduction

   • 1.1 Motivation for Intrinsic Image Decomposition
   • 1.2 The Role of Entropy in Image Processing
   • 1.3 Overview of Existing Methods for Intrinsic Image Estimation
   • 1.4 Key Challenges in Intrinsic Image Decomposition
   • 1.5 Contributions of the Paper
   • 1.6 Structure of the Paper

2. Related Work

   • 2.1 Early Approaches to Intrinsic Image Decomposition
   • 2.2 Optimization Techniques in Intrinsic Image Extraction
   • 2.3 Entropy-Based Methods in Image Processing
   • 2.4 Recent Advances in Reflectance and Shading Separation
   • 2.5 Limitations of Current Methods and the Need for Entropy Minimization

3. Problem Formulation

   • 3.1 Definition of Intrinsic Image Decomposition
   • 3.2 The Reflectance and Shading Models
   • 3.3 The Concept of Entropy in the Context of Image Decomposition
   • 3.4 Objective Function for Entropy Minimization
   • 3.5 Assumptions and Constraints in the Proposed Method

4. Entropy Minimization Framework

   • 4.1 Overview of Entropy and its Significance in Image Processing
   • 4.2 Formulation of the Entropy Minimization Problem
   • 4.3 Probabilistic Interpretation of Intrinsic Image Components
   • 4.4 Entropy Regularization and its Role in Image Decomposition
   • 4.5 Optimization Strategies for Entropy Minimization
   • 4.6 Computational Challenges and Solutions

5. Methodology for Intrinsic Image Decomposition

   • 5.1 Initial Image Segmentation and Preprocessing
   • 5.2 Estimating Reflectance and Shading via Entropy Minimization
   • 5.3 Handling Noise and Inconsistencies in the Image
   • 5.4 Incorporating Edge and Texture Information in the Decomposition
   • 5.5 Constraints and Penalties in the Optimization Problem
   • 5.6 Final Steps in Reflectance and Shading Separation

6. Algorithm Design and Implementation

   • 6.1 Step-by-Step Algorithm for Entropy Minimization in Intrinsic Image Decomposition
   • 6.2 Iterative Optimization Process for Reflectance and Shading Estimation
   • 6.3 Computational Considerations and Efficiency
   • 6.4 Parallelization or Approximation Techniques for Large-Scale Images
   • 6.5 Implementation Details and Code Availability

7. Experimental Setup and Evaluation

   • 7.1 Datasets for Intrinsic Image Decomposition
   • 7.2 Evaluation Metrics (e.g., Reflectance Accuracy, Shading Estimation)
   • 7.3 Baseline Methods and Comparison Criteria
   • 7.4 Experimental Protocols and Testing Scenarios
   • 7.5 Performance Analysis for Various Image Types and Conditions

8. Results and Discussion

   • 8.1 Quantitative Results of Intrinsic Image Decomposition
   • 8.2 Visual Results: Comparison with Ground Truth and Existing Methods
   • 8.3 Analysis of Reflectance and Shading Estimation Accuracy
   • 8.4 Comparison of Entropy Minimization with Other Optimization Approaches
   • 8.5 Impact of Entropy Minimization on Handling Shadows, Specularity, and Textures
   • 8.6 Limitations and Challenges in Practical Applications

9. Applications of Intrinsic Image Decomposition

   • 9.1 Computer Graphics and Rendering
   • 9.2 Image-Based Lighting and Relighting
   • 9.3 Object Recognition and Scene Understanding
   • 9.4 Image Editing and Enhancement
   • 9.5 Other Applications in Augmented Reality and Vision-Based Systems

10. Challenges and Future Directions

• 10.1 Addressing Real-World Complexity in Images (Lighting Variations, Complex Scenes)
• 10.2 Handling Multiple Light Sources and Dynamic Environments
• 10.3 Improving Efficiency for Real-Time Processing
• 10.4 Incorporating Deep Learning with Entropy Minimization for Better Performance
• 10.5 Future Research Directions in Intrinsic Image Decomposition

11. Conclusion

• 11.1 Summary of Contributions and Findings
• 11.2 Practical Implications of the Proposed Method
• 11.3 Limitations of the Current Approach
• 11.4 Final Remarks and Vision for Future Work

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