Hough-SIFT: Robust Image Registration for Linear Structures via Hough Space
arXiv:2607.14598
The paper introduces Hough-SIFT, a method that matches SIFT descriptors in Hough space to improve image registration in scenes with strong linear structures.
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.
5 pages, 6 figures. Supplementary video is available as an ancillary file. Acknowledgment updated in v2. Accepted for oral presentation at the 29th Meeting on Image Recognition and Understanding (MIRU2026)