Texture Boundary Detection for Real Time Tracking 1st edition by Ali Shahrokni, Tom Drummond, Pascal Fua ISBN 3540219835 9783540219835

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ISBN 10:       3540219835
ISBN 13: 978-3540219835
Author:         Ali Shahrokni, Tom Drummond, Pascal Fua
 

We propose an approach to texture boundary detection that only requires a line-search in the direction normal to the edge. It is therefore very fast and can be incorporated into a real-time 3–D pose estimation algorithm that retains the speed of those that rely solely on gradient properties along object contours but does not fail in the presence of highly textured object and clutter.

This is achieved by correctly integrating probabilities over the space of statistical texture models. We will show that this rigorous and formal statistical treatment results in good performance under demanding circumstances

Texture Boundary Detection for Real-Time Tracking 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 The Role of Texture in Visual Tracking
  • 1.3 Challenges in Texture Boundary Detection for Real-Time Applications
  • 1.4 Objectives and Contributions of the Paper
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Overview of Texture Boundary Detection Techniques
  • 2.2 Real-Time Tracking Approaches in Computer Vision
  • 2.3 Feature-based vs. Region-based Tracking
  • 2.4 Methods for Combining Texture Detection and Tracking
  • 2.5 Limitations of Current Techniques and Open Challenges

• Mathematical Foundations

  • 3.1 Texture Representation and Features
  • 3.2 Boundary Detection: Definitions and Key Concepts
  • 3.3 Local vs. Global Texture Analysis
  • 3.4 Motion Models for Real-Time Tracking
  • 3.5 Mathematical Formulation for Texture Boundary Detection in Tracking

• Texture Boundary Detection Methods

  • 4.1 Overview of Boundary Detection Algorithms
  • 4.2 Gradient-based Methods for Boundary Detection
  • 4.3 Wavelet and Multi-scale Texture Features for Boundary Detection
  • 4.4 Edge Detection vs. Texture Boundary Detection
  • 4.5 Incorporating Temporal Information for Real-Time Boundary Detection

• Real-Time Tracking Framework

  • 5.1 Overview of Real-Time Tracking Systems
  • 5.2 Integration of Texture Boundary Detection with Tracking Algorithms
  • 5.3 Temporal Coherence and Motion Estimation in Real-Time Tracking
  • 5.4 Robustness to Occlusions and Illumination Variations
  • 5.5 Tracking Strategies: Kalman Filters, Particle Filters, and Others

• Algorithm Design and Implementation

  • 6.1 Overview of the Proposed Boundary Detection and Tracking Pipeline
  • 6.2 Image Preprocessing and Feature Extraction
  • 6.3 Real-Time Texture Boundary Detection Algorithm Design
  • 6.4 Integration with Tracking Algorithms (e.g., Lucas-Kanade, Mean-Shift)
  • 6.5 Computational Efficiency and Optimization for Real-Time Operation

• Experimental Setup and Evaluation

  • 7.1 Datasets and Benchmarking for Texture Boundary Detection and Tracking
  • 7.2 Evaluation Metrics: Accuracy, Speed, and Robustness
  • 7.3 Experimental Protocols and Test Cases
  • 7.4 Comparison with Existing Methods in Real-Time Tracking
  • 7.5 Performance in Dynamic and Challenging Environments

• Results and Discussion

  • 8.1 Visual Examples of Texture Boundary Detection and Tracking
  • 8.2 Quantitative Results: Accuracy of Boundary Detection and Tracking Performance
  • 8.3 Speed and Efficiency of the Real-Time Implementation
  • 8.4 Analysis of the Impact of Texture Boundary Detection on Tracking Robustness
  • 8.5 Discussion of Results: Strengths and Weaknesses of the Proposed Approach

• Applications of Texture Boundary Detection in Real-Time Tracking

  • 9.1 Applications in Object Tracking and Detection
  • 9.2 Use in Augmented Reality and Interactive Systems
  • 9.3 Robotics and Autonomous Navigation
  • 9.4 Human-Computer Interaction and Gesture Recognition
  • 9.5 Applications in Surveillance and Security Systems

• Challenges and Future Directions

  • 10.1 Handling Complex Textures and Dynamic Environments
  • 10.2 Improving Robustness to Occlusions, Clutter, and Noise
  • 10.3 Real-Time Performance with High-Resolution Videos and Large Scenes
  • 10.4 Integration with Deep Learning Models for Improved Boundary Detection
  • 10.5 Future Research Directions in Real-Time Texture Boundary Detection and Tracking

• Conclusion

  • 11.1 Summary of Key Contributions and Findings
  • 11.2 Practical Implications of Texture Boundary Detection in Real-Time Tracking
  • 11.3 Limitations and Open Problems
  • 11.4 Final Remarks and Future Work

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