This project is part of a computer vision task where we explore various feature extraction and image matching techniques. The goal is to understand and implement key algorithms in computer vision, such as the Harris operator, lambda operator, and Scale-Invariant Feature Transform (SIFT).
The application provides the following features:
- Harris Operator: A technique for identifying corner points in images by analyzing intensity variations.
- Lambda (λ-) Operator: Used for detecting dark structures in images, especially thin and elongated structures like blood vessels.
- Key Points Generator with SIFT: Identifies key points that are stable across different scales, rotations, and illuminations.
- SIFT Matching: Matches images with different scales, illuminations, and sizes using SIFT descriptors.
- Template Matching Using Similarity: Locates a template image within a larger image using similarity measures like SSD and NCC.
Here are some outputs generated from the algorithms implemented in this project:
To run the project, you need:
C++ compiler Qt framework OpenCV library Run the Project
| Team Members' Names |
|---|
| Ahmed Kamal |
| Amgad Atef |
| Mahmoud Magdy |
| Mahmoud Mohamed |