AI 中文总结
研究图像配准问题，针对SIFT在强线性结构场景中失效的情况，提出Hough-SIFT方法，通过在霍夫空间进行SIFT描述符匹配，恢复描述符可辨别性，该方法在特殊场景中稳健且在正常场景中保持准确性。
AI 中文摘要
图像配准在电子图像稳定等应用中至关重要。尺度不变特征变换（SIFT）是常用的局部关键点检测器和描述符，通常能提供准确配准，但在具有强线性结构（如百叶窗）的场景中常失败，因为局部特征变得模糊。我们提出Hough-SIFT，一种在霍夫空间中进行SIFT描述符匹配的鲁棒配准方法。在此域中，线性结构形成独特峰值，恢复描述符可辨别性。实验表明，Hough-SIFT在SIFT常失败的线性场景中稳健，在正常场景中保持与SIFT相当的准确性。
英文摘要
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.
Comments5 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)
