Α-shapes for local feature detection. (February 2016) - Varytimidis, Christos; Rapantzikos, Konstantinos; Avrithis, Yannis; Kollias, Stefanos
Abstract: Local image features are routinely used in state-of-the-art methods to solve many computer vision problems like image retrieval, classification, or 3D registration. As the applications become more complex, the research for better visual features is still active. In this paper we present a feature detector that exploits the inherent geometry of sampled image edges using α - shapes . We propose a novel edge sampling scheme that exploits local shape and investigate different triangulations of sampled points. We also introduce a novel approach to represent the anisotropy in a triangulation along with different feature selection methods. Our detector provides a small number of distinctive features that is ideal for large scale applications, while achieving competitive performance in a series of matching and retrieval experiments. Abstract : Highlights: A novel edge sampling scheme is proposed that exploits local shape. Anisotropically weighted alpha shapes are introduced. Distinctive local features are selected from the alpha filtration. Competitive results are obtained for image matching and retrieval.