Publication: Computer Vision Projects

Welcome to my publication for my Computer Vision Projects ! This publication showcases a collection of projects focusing on various aspects of computer vision, including edge detection, feature detection, and image matching. These projects highlight fundamental techniques and concepts used in modern computer vision workflows, making this repository a valuable resource for both learning and experimentation.

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📸 Project 1: Manual Image Edge Detection with Python and OpenCV

This project delves into edge detection—a cornerstone of image processing. Implementing fundamental techniques offers a hands-on exploration of concepts such as convolution, gradient calculation, and thresholding.

✨ Key Features:

• 📌 Implementation of manual convolution for custom filters (e.g., Gaussian smoothing, Sobel edge detection).
• 📌 Computation of Sobel gradients to highlight edges.
• 📌 Application of various thresholding techniques, including single, double, and adaptive thresholding.
• 📌 Histogram analysis for gradient magnitude visualization and threshold optimization.

📚 Educational Value:
Ideal for those seeking to understand the principles of image edge detection and how convolution-based techniques work. The modular and well-commented code encourages experimentation with parameters like kernel sizes and thresholds.

🚀 Future Directions:
The project can be extended to include advanced edge detection algorithms like Canny or adapted for real-time applications.

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🖼️ Project 2: Local Feature Detection and Matching for Object Recognition

This project explores feature detection and matching—key techniques for object recognition, image stitching, and more. It specifically demonstrates detecting and matching instances of a specific object across multiple images.

✨ Key Features:

• 🧩 Harris Corner Detection: Identifies interest points in images.
• 🧩 ORB Descriptors: Encodes local image regions for robust matching across variations.
• 🧩 Feature Matching: Implements two methods:
    - 🔗 Sum of Squared Differences (SSD) for basic matching.
    - 🔗 Ratio Test for improved robustness.
• 🧩 Visualization tools for displaying detected features and matches.
• 🧩 Benchmarking with test images under varying conditions like rotation, scale, and illumination.

📚 Educational Value:
A comprehensive introduction to local feature detection and matching, with modular code for easy experimentation. Functions are clearly named and documented, ensuring accessibility for learners.

🚀 Future Directions:
Planned enhancements include exploring advanced matching techniques (e.g., FLANN) and adapting the project for real-time object detection and tracking.

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🤝 Contribution

Contributions are welcome! If you have ideas for improving existing projects or want to add new computer vision implementations, feel free to fork the repository and submit a pull request.

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💡 This repository is designed to grow as I continue to explore and implement new techniques in computer vision. Stay tuned for more exciting projects!