🎯 Principal Component Analysis (PCA) for Computer Vision

📝 Overview

This repository provides an intuitive and interactive exploration of Principal Component Analysis (PCA) in the context of computer vision. It demonstrates how PCA can be used to detect an object's position and orientation in images and track these attributes over time.

The project is presented as a fully interactive website built with HTML, CSS, and JavaScript, leveraging p5.js for dynamic visualizations and animations.

✨ Features

• 📖 Detailed mathematical explanations of PCA from first principles.
• 🎥 Interactive animations built with p5.js to enhance understanding.
• 🛠️ Hands-on experiments where users can tweak parameters and observe real-time changes.
• 🔍 Practical applications of PCA for object tracking in images.

🖥️ Live Demo

🚀 Try it out here: Link To Website

📌 How It Works

1. Mathematical Foundations 🧮

   • Step-by-step breakdown of PCA and its role in feature extraction.
   • Eigenvectors, eigenvalues, and their interpretation in image analysis.

2. Object Detection & Tracking 🎯

   • Using PCA to find the principal axes of an object in an image.
   • Estimating object orientation and position over time.

3. Interactive Visualizations 🎨

   • Real-time animations allowing users to modify parameters dynamically.
   • Intuitive demonstrations of eigenvectors and variance.

🎯 Use Cases

• Understanding PCA intuitively through interactive demos.
• Applying PCA in computer vision tasks like object detection and tracking.
• Learning how eigenvectors and eigenvalues relate to real-world image analysis.

🏗️ Future Improvements

• ✅ Add more real-world datasets for demonstrations.
• ✅ Implement PCA with real-time webcam input.
• ✅ Provide additional case studies with diverse object types.

📜 License

This project is licensed under the MIT License—free to use and modify! 🎉