# FFD: Fast Feature Detector

arXiv: https://arxiv.org/abs/2012.00859 · HTML: https://mlsanity.com/paper/2012.00859/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Adversarial Distortion Learning for Medical Image Denoising](https://mlsanity.com/paper/2204.14100/) (2022)
- [No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy](https://mlsanity.com/paper/2401.08115/) (2024)
- [Image Feature Information Extraction for Interest Point Detection: A Review](https://mlsanity.com/paper/2106.07929/) (2021)
- [Scale-Free Image Keypoints Using Differentiable Persistent Homology](https://mlsanity.com/paper/2406.01315/) (2024)
- [EasyVis2: A Real Time Multi-view 3D Visualization System for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose](https://mlsanity.com/paper/2412.16742/) (2024)

## Abstract (arXiv)

Scale-invariance, good localization and robustness to noise and distortions are the main properties that a local feature detector should possess. Most existing local feature detectors find excessive unstable feature points that increase the number of keypoints to be matched and the computational time of the matching step. In this paper, we show that robust and accurate keypoints exist in the specific scale-space domain. To this end, we first formulate the superimposition problem into a mathematical model and then derive a closed-form solution for multiscale analysis. The model is formulated via difference-of-Gaussian (DoG) kernels in the continuous scale-space domain, and it is proved that setting the scale-space pyramid's blurring ratio and smoothness to 2 and 0.627, respectively, facilitates the detection of reliable keypoints. For the applicability of the proposed model to discrete images, we discretize it using the undecimated wavelet transform and the cubic spline function. Theoretically, the complexity of our method is less than 5% of that of the popular baseline Scale Invariant Feature Transform (SIFT). Extensive experimental results show the superiority of the proposed feature detector over the existing representative hand-crafted and learning-based techniques in accuracy and computational time. The code and supplementary materials can be found at~urlhttps://github.com/mogvision/FFD.