A novel fast retina keypoint extraction algorithm for multispectral images using geometric algebra

Abstract

The feature extraction for multispectral images plays an important role in many computer vision applications. Recently, geometric algebra (GA) based scale invariant feature transform algorithm (GA-SIFT) and GA based speeded up robust Features algorithm (GA-SURF), have been proposed to extract feature of multispectral image in GA space. However those methods are difficult to be implemented in real-time applications. Now, the challenge is to design a new algorithm to extract the features of multispectral image more efficiently and quickly, so that it can be used in real-time applications. Although the proposed fast retina keypoint (FREAK) algorithm is faster to compute and more robust than SIFT and SURF, it can not be utilized to extract features directly for multispectral images. In this paper, we propose a novel fast retina keypoint extraction algorithm based on GA, named as GA-FREAK, for multispectral images. Firstly, the multispectral images are represented as multivectors in GA space, then the interest points are detected with the procedure of FREAK in GA space. Finally, our experiments demonstrate that the GA-FREAK is faster and more robust than some previous algorithms in multispectral images. It is expected that the proposed GA-FREAK will be a competitive alternative in real-time applications of multispectral images.