@inproceedings{fd14394747994183a9540b29157c7af9,
title = "Change Detection via Graph Matching and Multi-View Geometric Constraints",
abstract = "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.",
keywords = "Change detection, Geometric constraints, Maximum common subgraph matching, Multi-view",
author = "Jiwei Shen and Shujing Lyu and Xiaofeng Zhang and Yue Lu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ICIP.2019.8803527",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "4035--4039",
booktitle = "2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings",
address = "美国",
}