This repository collects various image processing algorithms, providing practical examples and implementations. The projects are designed to showcase both foundational and advanced techniques in image processing.
Most implementations, such as High Dynamic Range Imaging, Feature Detection and Feature Description, and Applications of Digital Image Processing, are dependency-light and run without OpenCV, emphasizing simplicity and efficiency.
In contrast, advanced implementations like Image Classification and Document Scanner leverage OpenCV to demonstrate more complex applications of image processing. This repository serves as a portfolio of my work in image processing.
- High Dynamic Range Imaging: An implementation of HDR imaging techniques.
- Feature Detection and Feature Description: Methods for detecting and describing features in images.
- Applications of Digital Image Processing: A collection of digital image processing techniques, including transformations, color models, and Fourier transforms.
- Image Classification: A tutorial on image classification using machine learning and image processing.
- Document Scanner: A standalone EXE file for scanning and rectifying document images.
This project demonstrates the use of machine learning and image processing techniques to classify images. It serves as an educational tutorial and includes detailed step-by-step instructions.
Explore techniques to create HDR images. This project emphasizes effective HDR generation without relying on OpenCV dependencies.
This project implements feature detection using algorithms like Harris Corner and Multiscale Harris Corner, along with feature description methods. It includes examples to demonstrate the results.
A practical tool packaged as an executable file (EXE) that scans and rectifies document images. The GUI allows users to adjust brightness, contrast, and rotation for better results.
This section includes various digital image processing techniques implemented in Python.
- GUI Example:
- K-means Segmentation:
- Model Visualization:
Each project is accompanied by examples and explanations to help users understand and experiment with image processing techniques. Dive into the repository for detailed implementations and educational resources.