SIFT Saliency Analysis for Matching Repetitive Structures

Abstract

The ambiguity resulting from repetitive structures in a scene presents a major challenge for image matching. This paper proposes a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images with repetitive structures. The feature saliency within the reference image is estimated by analyzing feature stability and dissimilarity via MonteCarlo simulation. In the proposed method, feature matching is performed only within the region of interest to reduce the ambiguity caused by repetitive structures. The experimental results demonstrate the efficiency and robustness of the proposed method, especially in the presence of respective structures.

Integrated Velocity Measurement Algorithm Based on Optical Flow and Scale-Invariant Feature Transform

“…To obtain higher homing precision for local robot navigation, a novel robot visual homing algorithm was proposed that combined SIFT features with the warping method [19], and the effectiveness of the proposed algorithm was verified experimentally. Reference [20] proposed a matching method based on SIFT feature saliency analysis to achieve robust feature matching between images that contained repetitive structures; feature matching was only to be performed within the region of interest to reduce the ambiguity caused by the repetitive structures, and the experimental results demonstrated the efficiency and robustness of the proposed method. While SIFT has wide application prospects in navigation, its low accuracy and poor robustness have limited further development.…”