vishnoitanuj / CV-Feature-Extraction

ORB implementation for feature matching

Oriented FAST and Rotated BRIEF based Feature Matching

Oriented FAST and Rotated BRIEF (ORB) is basically a fusion of FAST keypoint detector and BRIEF descriptor with many modifications to enhance the performance. First it use FAST to find keypoints, then apply Harris corner measure to find top N points among them. It also use pyramid to produce multiscale-features. I will be using opencv-python for the implementation of the assignment and algorithm.

Major advantages of the ORB:

• Scale Invariance
• Rotational Invariance
• Illumination Invariance
• Noise Invariance

The final json is contained in file final_data.json

Installing requirements.

1. python version 3.5.1
2. pip version 19.1.1
3. Preferred OS: Ubuntu 16.04 (tested)

Now go to the directly and run the following command:

• pip install -r requirements.txt

Running the code

The final code lies in the file get_json.py

For step wise understanding the ORB code please check: ORB- Feature Matcher

So just run the file to get the output as final_data.json

Code details

The algorithm is majorly implemented in file feature_match.py, which contain the feature matching orb algorithm and also the outlier removal code.

For more insight into code implementation, please check the assignment report Report