Robust scene matching using line segments
Jingying Chen, Zuomin Guo, Paul Tan, Terence K. L. Goh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
Scene matching is of great interest in the pattern recognition and computer vision domain. Some researchers investigated the problem of scene matching, by employing phase correlation and neural network. Although varying good results have been obtained, noise and occlusion appear to affect the robustness of these matching algorithms. In order to develop a scene matching system that is robust to adverse condition (i.e. occlusion and added noise) and produces intuitively reasonable results, a robust scene matching system based on line segments is proposed in this paper. Since line pattern is effective for scene representation and matching, the proposed system employs a two-stage hierarchy, i.e. line segmentation and matching. In the first stage, the raw scenes are transformed into line segment maps (LSM); in the second stage, the Line Segment Hausdorff Distance (LHD) measure is applied to generate the matches. The line segmentation approach is based on robust shape feature and tends to generate more consistent LSM, while the LHD has the advantage to incorporate structural and spatial information to compute dissimilarity between two sets of line segments rather than two sets of points. Encouraging results have been obtained with aerial images.