This is the implementation of the paper "Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution" that is published in Pattern Recognition Letters (PRL). Alternatively, see TL;DR version.
While competing ANMS methods have similar performance in terms of spatial keypoints distribution, the proposed method SSC is substantially faster and scales better:
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Here is how proposed ANMS method visually compares to traditional methods: TopM | Bucketing | SSC (proposed)
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Related algorithms that are implemented in this repository are:
- "Visual Odometry based on Stereo Image Sequences with RANSAC-based Outlier Rejection Scheme" - bucketing
- "Multi-Image Matching using Multi-Scale Oriented Patches" - original ANMS
- "Efficiently selecting spatially distributed keypoints for visual tracking" - more efficient ANMS
For more details about the algorithm, experiments as well as the importance of homogeneously distributed keypoints for SLAM please refer to the paper.
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Clone this repository:
git clone https://github.com/BAILOOL/ANMS-Codes.git. See codebase visualization to better understand code repository structure. -
Choose your language:
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Make sure the path to test image is set correctly
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Run produced executable
./ANMS_Codesfor C++ or relevant script for other languages
Codes have been tested with OpenCV 2.4.8, OpenCV 3.3.1, OpenCV 4.2.0 and Ubuntu 14.04, 16.04, 20.04.
Follow instructions in docs/contributing.
If you use these codes in your research, please cite:
@article{bailo2018efficient,
title={Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution},
author={Bailo, Oleksandr and Rameau, Francois and Joo, Kyungdon and Park, Jinsun and Bogdan, Oleksandr and Kweon, In So},
journal={Pattern Recognition Letters},
volume={106},
pages={53--60},
year={2018},
publisher={Elsevier}
}