Fractional order method of image keypoints detection
Grzegorz Sarwas, Sławomir Skoneczny, Grzegorz Kurzejamski · 2017
In this paper we propose a novel efficient method of characteristic image point detection based on the fractional order derivative. The concept of this approach called (FSIFT: Fractional-SIFT) is inspired by the Scale-Invariant Feature Transform (SIFT) proposed by Lowe and can be viewed as a certain generalization of this formula. The classical SIFT detector is implemented efficiently by using a difference of Gaussian (DoG) functions applied to image, in order to identify potential interest points. This difference is an approximation of the LoG (Laplacian of Gaussians) operator, which can be treated as the sum of the second order derivatives of the Gaussian image. In our method we take advantage of the fractional order derivative performed on the Gaussian images. In order to extract distinctive invariant features we have omitted the step of calculating DoG images. Instead of it, we have applied the fractional derivatives of different orders not far from the values of two. We have chosen the robust and efficient method of calculating the fractional order derivative using the Fourier domain. Many practical experiments have been performed. The promising results of our approach have been compared with the results of application of the well known algorithms like SURF and SIFT.