This repository contains solutions and projects for the Computer Vision course. Throughout this course, various concepts and techniques related to computer vision have been explored, implemented, and organized into assignments and projects.
Develop a graphical interface allowing users to place points, perform 2D translations, and display transformed points.
Implement gamma correction, Prewitt edge detection, and Discrete Cosine Transform (DCT) on images.
Construct a flowchart and program implementing K-means clustering. Apply it to the IRIS dataset.
Conduct classification/regression analyses on "cars" and "heart" datasets using multiple methods and cross-validation.
The projects directory contains detailed READMEs describing various computer vision-related projects.
- Python (version 3.6 or higher)
- Virtual Environment (recommended)
- Additional requirements specified in assignment or project READMEs.
Refer to individual assignment or project directories for specific instructions. Each task may require different libraries or data, so reading corresponding README files is crucial for guidance.
Contribute by forking the repository, creating a new branch for features or bug fixes, making changes, testing thoroughly, and creating a pull request. Follow coding standards and guidelines.
This project is licensed under the MIT License. Feel free to use these resources for your computer vision studies or share them. If helpful, consider giving this repository a star!