Robust scale-invariant feature extraction

Abstract

The stability of feature matching is a fundamental problem for many robotic tasks, such as visual servoing and navigation. This paper presents a new feature extractor which is able to improve the robustness of feature matching under large scale change. The new extractor consists of a fast and scalable Laplacian of Gaussian (LoG) approximator based on blocky Mexican hat wavelet, and an optimized sampling distribution for the features in the multi-resolution scale space. The sampling distribution is a critical factor to boosting the matching rate, however it was not discussed in depth by the studies in recent years. For evaluation, the new algorithm is compared with SIFT and SURF, it demonstrates a significant matching rate improvement.