Improving local features by dithering-based image sampling

Abstract

The recent trend of structure-guided feature detectors, as opposed to blob and corner detectors, has led to a family of methods that exploit image edges to accurately capture local shape. Among them, the WαSH detector combines binary edge samplingwith gradient strength and computational geometry representations towards distinctive and repeatable local features. In this work, we provide alternative, variable-density sampling schemes on smooth functions of image intensity based on dithering. These methods are parameter-free and more invariant to geometric transformations than uniform sampling. The resulting detectors compare well to the state-of-the-art, while achieving higher performance in a series of matching and retrieval experiments.