A New Scheme for Keypoint Detection and Description

Abstract EN

The keypoint detection and its description are two critical aspects of local keypoints matching which is vital in some computer vision and pattern recognition applications.

This paper presents a new scale-invariant and rotation-invariant detector and descriptor, coined, respectively, DDoG and FBRK.

At first the Hilbert curve scanning is applied to converting a two-dimensional (2D) digital image into a one-dimensional (1D) gray-level sequence.

Then, based on the 1D image sequence, an approximation of DoG detector using second-order difference-of-Gaussian function is proposed.

Finally, a new fast binary ratio-based keypoint descriptor is proposed.

That is achieved by using the ratio-relationships of the keypoint pixel value with other pixel of values around the keypoint in scale space.

Experimental results show that the proposed methods can be computed much faster and approximate or even outperform the existing methods with respect to performance.