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OPTIMIZATION OF SOFTWARE-BASED EDGE DETECTION

Abstract:

Edge detection is a fundamental task in image processing and computer vision, crucial for identifying boundaries and shapes within digital images. This study focuses on optimizing software-based edge detection algorithms to enhance accuracy, efficiency, and applicability across various applications.

The research explores different edge detection methods, including traditional techniques like Sobel, Prewitt, and Canny, as well as more advanced algorithms based on machine learning and deep learning models. The optimization process involves refining these algorithms to improve edge detection performance in terms of precision, speed, and robustness against noise and varying image conditions.

Key aspects of the optimization include algorithmic enhancements, parameter tuning, and leveraging parallel processing techniques using GPUs or multi-core processors to accelerate computation. Additionally, the study investigates the integration of edge detection algorithms into real-time systems and embedded platforms for practical deployment.

Evaluation of the optimized algorithms is conducted through comparative analysis with standard benchmarks and datasets. Metrics such as accuracy, computational efficiency, and runtime performance are assessed to validate improvements achieved through optimization.

The findings highlight the effectiveness of optimized software-based edge detection in enhancing image analysis capabilities across fields such as medical imaging, autonomous driving, surveillance, and industrial automation. This research contributes to advancing the state-of-the-art in edge detection technology, offering insights and methodologies beneficial for researchers, developers, and practitioners in the field of computer vision and image processing.

Table of Contents

Chapter 1: Introduction

1.1 Background of Edge Detection in Image Processing

1.2 Importance and Applications of Edge Detection

1.3 Challenges in Software-Based Edge Detection

1.4 Objectives of the Study

1.5 Scope and Organization of the Thesis

Chapter 2: Literature Review

2.1 Overview of Edge Detection Techniques

2.1.1 Classical Methods (Sobel, Prewitt, Roberts)

2.1.2 Advanced Methods (Canny Edge Detector, Laplacian of Gaussian)

2.1.3 Machine Learning and Deep Learning Approaches

2.2 Optimization Strategies in Image Processing

2.2.1 Algorithmic Enhancements

2.2.2 Parallel Processing and Hardware Acceleration

2.2.3 Integration with Real-Time Systems

2.3 Comparative Analysis of Edge Detection Algorithms

2.4 Summary of Key Findings

Chapter 3: Methodology and Optimization Techniques

3.1 Selection of Edge Detection Algorithms for Optimization

3.2 Algorithmic Enhancements and Parameter Optimization

3.3 Utilization of Parallel Processing Techniques

3.3.1 GPU Computing for Edge Detection

3.3.2 Multi-Core CPU Optimization

3.4 Integration into Real-Time and Embedded Systems

3.5 Experimental Design and Setup

3.6 Performance Metrics and Evaluation Criteria

3.7 Ethical Considerations in Algorithm Optimization

3.8 Summary

Chapter 4: Implementation and Results

4.1 Experimental Setup and Dataset Description

4.2 Implementation Details of Optimized Edge Detection Algorithms

4.3 Performance Evaluation Metrics

4.3.1 Accuracy Assessment

4.3.2 Computational Efficiency and Runtime Performance

4.4 Comparative Analysis with Baseline Algorithms

4.5 Discussion of Results and Interpretation

4.6 Summary

Chapter 5: Conclusion and Future Directions

5.1 Summary of Achievements

5.2 Contributions to the Field of Image Processing

5.3 Practical Applications and Implications

5.4 Limitations and Challenges Encountered

5.5 Recommendations for Future Research

5.6 Conclusion

References

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