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Finding Objects with OpenCV

Overview

This project implements object detection using OpenCV, utilizing techniques like SIFT, FLANN, RANSAC, and Homography to locate objects within images. The goal is to identify key features and match them across different images for accurate object localization.

Features

  • Key Feature Matching: Uses SIFT for extracting and matching features.
  • Robust Model: FLANN and RANSAC for fast and reliable matching.
  • Object Localization: Applies Homography for accurate object detection.
  • Interactive Code: Includes Jupyter Notebook for hands-on experimentation.

Project Structure

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

Technologies Used

  • Python: Programming language
  • OpenCV: Computer vision library
  • Jupyter Notebook: Interactive environment for code execution
  • NumPy: For numerical operations

Results

The project demonstrates successful object detection, including:

  • Feature extraction and matching using SIFT
  • Homography-based transformation for object localization

License

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

Acknowledgments

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