🔍 Finding Objects with OpenCV

📌 Overview

This project demonstrates object detection using OpenCV, employing feature-matching techniques such as SIFT, FLANN, RANSAC, and Homography. The objective is to extract key features from an image and match them against another to achieve accurate object localization.

✨ Features

✅ Feature Extraction & Matching – Uses SIFT to detect and match key points across images.
✅ Robust Matching Algorithm – Implements FLANN and RANSAC for precise and efficient feature matching.
✅ Object Localization – Utilizes Homography to determine the object's position in different images.
✅ Interactive Experimentation – Includes a Jupyter Notebook for hands-on testing and visualization.

📁 Project Structure

Finding-Objects/  
│  
├── finding-objects.ipynb   # Jupyter Notebook with full implementation  
├── images/                 # Sample images for object detection  
├── README.md               # Project documentation  
└── requirements.txt        # List of dependencies  

🛠 Technologies Used

• Python – Core programming language
• OpenCV – Computer vision library for image processing
• NumPy – Efficient numerical operations
• Jupyter Notebook – Interactive coding environment

📊 Results

This project successfully detects objects by:
🔹 Extracting key features using SIFT
🔹 Matching features with FLANN and refining results with RANSAC
🔹 Transforming detected objects using Homography

📜 License

This project is open-source and available under the MIT License.

🙌 Acknowledgments

• Libraries Used: OpenCV, NumPy
• Techniques Implemented: SIFT, FLANN, RANSAC, Homography