Towards Adaptive Governance in Big Data Health Research

Abstract

Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Es- tablished leaders in the field are the SIFT and SURF al- gorithms which exhibit great performance under a variety of image transformations, with SURF in particular consid- ered as the most computationally efficient amongst the high- performance methods to date. In this paper we propose BRISK1, a novel method for keypoint detection, description and matching. A compre- hensive evaluation on benchmark datasets reveals BRISK’s adaptive, high quality performance as in state-of-the-art al- gorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.