Abstract. We believe that the potential of edges in local feature detection has not been fully exploited and therefore propose a detector that starts from single scale edges and produces reliable and interpretable blob-like regions and groups of regions of arbitrary shape. The detector is based on merging local maxima of the distance transform guided by the gradient strength of the surrounding edges. Repeatability and matching score are evaluated and compared to state-of-the-art detectors on standard benchmarks. Furthermore, we demonstrate the potential application of our method to wide-baseline matching and feature detection in sequences involving human activity.
Starting also from Canny edges, Rapantzikos et al . REF compute the binary distance transform and detect regions by a grouping process that is initialized by local maxima of the distance transform and guided by the gradient strength of nearby edges.
Detecting Regions from Single Scale Edges
ECCV Workshops
Computer Science