Dithering-based Sampling and Weighted α-shapes for Local Feature Detection

Local feature detection has been an essential part of many methods for computer vision applications like large scale image retrieval, object detection, or tracking. Recently, structure-guided feature detectors have been proposed, exploiting image edges to accurately capture local shape. Among them, the WαSH detector [Varytimidis et al., 2012] starts from sampling binary edges and exploits α-shapes, a computational geometry representation that describes local shape in different scales. In this work, we propose a novel image sampling method, based on dithering smooth image functions other than intensity. Samples are extracted on image contours representing the underlying shapes, with sampling density determined by image functions like the gradient or Hessian response, rather than being fixed. We thoroughly evaluate the parameters of the method, and achieve state-of-the-art performance on a series of matching and retrieval experiments.