Fast explicit diffusion for accelerated features in nonlinear scale spaces

TLDR: A novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces and introduces a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the non linear scale space, is scale and rotation invariant and has low storage requirements.

Abstract: We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces such as KAZE [1] and BFSIFT [6] are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) [3, 4] embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. Our features are called Accelerated-KAZE (A-KAZE) due to the dramatic speed-up introduced by FED schemes embedded in a pyramidal framework.