🤖 AI Summary
This work addresses the failure of conventional SIFT-based image registration in scenes dominated by strong linear structures, where local features become ambiguous and poorly discriminative. To overcome this limitation, the authors propose a novel approach that, for the first time, transfers SIFT descriptors into Hough space for matching. By leveraging the Hough transform, linear structures are mapped to prominent peaks, thereby restoring the distinctiveness of features. The resulting Hough-space feature matching framework significantly outperforms standard SIFT in highly structured environments while maintaining comparable registration accuracy in general scenes. This method effectively mitigates the performance degradation of SIFT in structured settings, offering a robust solution for reliable image alignment across diverse scenarios.
📝 Abstract
Image registration is essential in applications such as electronic image stabilization. Scale-Invariant Feature Transform (SIFT), a widely used local keypoint detector and descriptor, typically provides accurate registration; however, it often fails in scenes with strong linear structures (e.g., shutters), where local features become ambiguous. We propose Hough-SIFT, a robust registration method that performs SIFT descriptor matching in Hough space. In this domain, linear structures form distinctive peaks that restore descriptor discriminability. Experiments demonstrate that Hough-SIFT is robust in linear scenes where SIFT frequently fails, while maintaining accuracy comparable to SIFT in normal scenes.