FFD: Fast Feature Detector

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 https://github.com/mogvision/FFD.