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Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
| Date | Stars |
|---|---|
| 2026-07-24 | 352 |
| 2026-07-25 | 352 |
| 2026-07-28 | 352 |
| 2026-07-30 | 352 |
| 2026-08-06 | 352 |
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# Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
This is the implementation of the [paper](https://www.researchgate.net/publication/323388062_Efficient_adaptive_non-maximal_suppression_algorithms_for_homogeneous_spatial_keypoint_distribution) *"Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution"* that is published in Pattern Recognition Letters (PRL). Alternatively, see [TL;DR version](https://www.shortscience.org/paper?bibtexKey=journals/prl/BailoRJPBK18&a=ukrdailo).
While competing ANMS methods have similar performance in terms of spatial keypoints distribution, the proposed method SSC is substantially faster and scales better:
|  |  |  |
|:----:|:---:|:---:|
Here is how proposed ANMS method visually compares to traditional methods: TopM | Bucketing | SSC (proposed)
|  |  |  |
|:---:|:---:|:---:|
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](https://www.researchgate.net/publication/323388062_Efficient_adaptive_non-maximal_suppression_algorithms_for_homogeneous_spatial_keypoint_distribution).
## How to run
1. Clone this repository: `git clone https://github.com/BAILOOL/ANMS-Codes.git`. See [codebase visualization](https://octo-repo-visualization.vercel.app/?repo=BAILOOL%2FANMS-Codes) to better understand code repository structure.
2. Choose your language:
- [C++](https://github.com/BAILOOL/ANMS-Codes/tree/master/C++)
- [Python](https://github.com/BAILOOL/ANMS-Codes/tree/master/Python)
- [Matlab](https://github.com/BAILOOL/ANMS-Codes/tree/master/Matlab) [](https://uk.mathworks.com/matlabcentral/fileexchange/88723-anms-codes)
- [Java](https://github.com/BAILOOL/ANMS-Codes/tree/master/Java)
3. Make sure the [path to test image](C++/CmakeProject/main.cpp#L8) is set correctly
4. Run produced executable `./ANMS_Codes` for 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`.
## Contributing
Follow instructions in [docs/contributing](https://github.com/BAILOOL/ANMS-Codes/blob/master/docs/contributing.rst).
## Citation
If you use these codes in your research, please cite:
```text
@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}
}
```
Excerpt of 3,941 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:75c0dc329f3b23f4, topic:robotics, topic:slam, readme:slam
matched fp:75c0dc329f3b23f4, topic:computer-vision, topic:opencv