Computer Vision Coursework

This repository contains the coursework project for the Computer Vision module at Imperial College London. The project explores key techniques in computer vision, including feature extraction, feature matching, and 3D surface reconstruction.

Overview

The notebook implements:

1. Salient Feature Detection: Using SIFT to detect and describe features in video frames.
2. Feature Matching: Finding correspondences between frames using descriptor matching.
3. Epipolar Geometry: Estimating the fundamental matrix and applying the epipolar constraint.
4. 3D Surface Reconstruction: Estimating disparities and reconstructing the scene in 3D.

Features

• Image Processing: Includes grayscale conversion, feature detection, and visualization of keypoints.
• Visualization: Saves and displays results such as detected keypoints and matched features.
• Mathematical Computation: Computes the fundamental matrix and compares different estimation methods.
• Surface Area Estimation: Uses disparity maps and 3D reconstruction to calculate geometric properties like the area of a swimming pool.

Getting Started

Prerequisites

• Python 3.8+
• Required libraries:

  pip install numpy opencv-python matplotlib

Usage

1. Clone the repository:

   git clone <repository_url>
   cd <repository_name>

2. Run the Jupyter Notebook to execute the code and view results:

   jupyter notebook Computer_Vision_Coursework.ipynb

Results

The project includes the following outputs:

• Keypoint visualization.
• Matching features across frames.
• Fundamental matrix computation.
• Disparity maps and 3D reconstruction.

License

This project is for academic purposes and follows the coursework submission guidelines of Imperial College London. Please do not directly reuse the code for academic submissions without proper attribution.