ORB-feature-matching

Python implementation of ORB feature matching algorithm from scratch. (not using openCV)

This is a python implementation of the ORB feature extraction/detection and matching without using OpenCV orb functions. It was done as an exercise of my understanding of the algorithm.

Code Structure

Inside the utils.py file I have created the following functions:

• FAST(): FAST algorithm [Features from Accelerated Segment Test] for keypoints (corners) detection.

• corner_orientations(): a function that computes the orientations of each keypoint based on the intensity centroid method.

• BRIEF(): BRIEF algorithm [Binary Robust Independent Elementary Features] for keypoints (corners) detection. The function can also utilize the keypoints orientations to compute the descriptors accordingly (steered-BRIEF algorithm).

• match(): Brute force matching of the BRIEF descriptors based on hamming distance, with the option to perform cross-check, and to remove ambiguous matches (confusing matches) using a distance_ratio.

The actual ORB implementation is in the ORB Notebook.ipynb file where I use all the functions of utils.py.

Examples

Multi-Scale Keypoints Detection

Scale Test Matching

Blur Test Matching

TODO:

The performance of this implementation is noticably worse (some incorrect matches & overall very slow) than the OpenCV ORB implementation, further improvements could be made in the future.