Computer Vision Implementations

This repository contains implementations of fundamental and advanced computer vision techniques, including image processing, edge detection, feature extraction, optical flow, and stereo vision. The projects utilize mathematical models and algorithms for visual data analysis and enhancement.

Features

• Image Processing – RGB channel extraction, grayscale conversion, histogram equalization
• Edge Detection – Sobel, Prewitt, and Laplacian operators
• Feature Detection – Harris corner detection, Hough transform for line and circle detection
• Optical Flow – Motion estimation using Lucas-Kanade method
• Stereo Vision – Disparity map generation and correlation matching
• Camera Calibration – Pose recovery through Essential Matrix using the 8-Point Algorithm

Getting Started

Prerequisites

• MATLAB (or OpenCV if using Python)
• Basic understanding of image processing concepts

Usage

Clone the repository and run the scripts in MATLAB or Python:

git clone https://github.com/miraydicle/Computer-Vision.git
cd Computer-Vision

For MATLAB:

run('script_name.m')

For Python:

python script_name.py

License

This project is licensed under the GNU General Public License v3.0 (GPL-3.0).

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Author: Miray Dicle