Sale!

Image Similarity Using Mutual Information of Regions 1st edition by Daniel Russakoff, Carlo Tomasi, Torsten Rohlfing, Calvin Maurer ISBN 3540219828 9783540219828

Name: Image Similarity Using Mutual Information of Regions 1st edition by Daniel Russakoff, Carlo Tomasi, Torsten Rohlfing, Calvin Maurer ISBN 3540219828 9783540219828
SKU: EB-11452
Availability: InStock

Original price was: $50.00.Current price is: $35.00.

Authors:Daniel B. Russakoff, Carlo Tomasi, Torsten Rohlfing; Calvin R. Maurer Jr. , Tags:Computer Vision – ECCV 2004 , Author sort:Daniel B. Russakoff, Carlo Tomasi, Torsten Rohlfing & Maurer, Calvin R. Jr. , Languages:Languages:eng , Published:Published:Mar 2004

SKU: EB-11452 Category: eBook PDF Tags: Calvin Maurer, Carlo Tomasi, Daniel Russakoff, Image, Information, Mutual, Regions 1st, Similarity, Torsten Rohlfing

Description

Image Similarity Using Mutual Information of Regions 1st edition by Daniel B. Russakoff, Carlo Tomasi, Torsten Rohlfing, Calvin R. Maurer Jr. – Ebook PDF Instant Download/Delivery. 3540219828, 978-3540219828
Full download Image Similarity Using Mutual Information of Regions 1st Edition after payment

Product details:

ISBN 10: 3540219828
ISBN 13: 978-3540219828
Author: Daniel B. Russakoff, Carlo Tomasi, Torsten Rohlfing, Calvin R. Maurer Jr.

Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding individual pixels and not those of each pixel’s respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain—intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.

Image Similarity Using Mutual Information of Regions 1st Table of contents:

1. Introduction
  - 1.1 Motivation and Background
  - 1.2 Importance of Image Similarity in Computer Vision
  - 1.3 The Concept of Mutual Information (MI)
  - 1.4 Region-Based Image Similarity
  - 1.5 Contributions and Objectives of the Paper
  - 1.6 Structure of the Paper
2. Related Work
  - 2.1 Image Similarity Measures in Computer Vision
  - 2.2 Mutual Information in Image Registration and Matching
  - 2.3 Region-Based Methods for Image Comparison
  - 2.4 Limitations of Existing Similarity Measures and the Need for MI-Based Region Matching
  - 2.5 Approaches Incorporating Spatial and Structural Information
3. Preliminaries
  - 3.1 Definition of Mutual Information
  - 3.2 Types of Mutual Information (e.g., Global, Local, Conditional)
  - 3.3 Overview of Regions in Image Processing
  - 3.4 Entropy and Joint Entropy Concepts
  - 3.5 The Role of MI in Quantifying Dependencies Between Regions
4. Problem Formulation
  - 4.1 Defining Image Similarity Based on Regions
  - 4.2 Formulation of Mutual Information for Regions of Interest
  - 4.3 Objectives of Region Matching Using MI
  - 4.4 Challenges in Extracting and Defining Regions from Images
  - 4.5 Assumptions and Constraints in the Proposed Approach
5. Mutual Information for Region-Based Image Similarity
  - 5.1 Overview of Mutual Information for Image Matching
  - 5.2 Region Selection and Segmentation Techniques
  - 5.3 Computing MI for Image Regions
  - 5.4 MI Normalization for Region Matching
  - 5.5 Handling Noise, Occlusion, and Variability in Regions
  - 5.6 Enhancements to MI for Region-Based Applications
6. Algorithm Design
  - 6.1 Step-by-Step Methodology for Region-Based Image Similarity
  - 6.2 Region Detection and Matching Using MI
  - 6.3 Optimization Techniques for Efficient MI Computation
  - 6.4 Parallelization or Approximation Techniques
  - 6.5 Algorithm for Multi-Scale Region Matching Using MI
7. Experimental Setup and Evaluation
  - 7.1 Datasets for Image Matching and Registration
  - 7.2 Evaluation Metrics (e.g., Similarity Accuracy, Matching Precision)
  - 7.3 Baseline Methods for Image Similarity Comparison
  - 7.4 Experimental Protocols and Testing Scenarios
  - 7.5 Performance Analysis of Region-Based MI Approach
8. Results and Discussion
  - 8.1 Quantitative Results for Image Matching Using MI
  - 8.2 Qualitative Results: Visual Comparison of Region-Based Similarity
  - 8.3 Performance Analysis in Various Image Scenarios (e.g., Deformations, Lighting Changes)
  - 8.4 Comparison with Other Image Similarity Measures
  - 8.5 Impact of Region Size, Overlap, and Choice on MI-Based Similarity
9. Applications
  - 9.1 Image Registration and Alignment
  - 9.2 Content-Based Image Retrieval
  - 9.3 Object Detection and Recognition
  - 9.4 Image Search and Recommender Systems
  - 9.5 Medical Imaging and Diagnosis
10. Challenges and Future Directions
- 10.1 Handling Complex Images with Multiple Regions and High Variability
- 10.2 Improving Robustness to Occlusion, Rotation, and Scaling
- 10.3 Real-Time Image Matching and Region Extraction
- 10.4 Combining MI with Deep Learning for Enhanced Image Similarity
- 10.5 Future Research Directions in Mutual Information-Based Methods
1. Conclusion
- 11.1 Summary of Contributions and Findings
- 11.2 Practical Implications of Region-Based MI for Image Similarity
- 11.3 Limitations and Future Work
- 11.4 Final Remarks

People also search for Image Similarity Using Mutual Information of Regions 1st :

image similarity using mutual information of regions
mutual information image registration
image-similarity-measures github
compare two images for similarity app
image-similarity-measures