Change Detection via Graph Matching and Multi-View Geometric Constraints
Jiwei Shen, Shujing Lyu, Xiaofeng Zhang, Yue Lu · 2019
Change detection is a critical preprocessing step of visual perception with broad prospects. Its primary challenge is to identify all the meaningful changes from a target image to the source image, which is observed of the same scene and has a different perspective as well. A robust change detection method involving graph matching and geometric constraints is proposed in this paper. Maximum common sub-graph matching is applied for alleviating the risk of suboptimal results and geometric constraints are used to remove the possible mistaken results. Detection results in different real-world scenes with respect to considerable textural moved objects show that the proposed method is more robust than the state-of-the-art methods.