@inproceedings{084fd6bc2ea4408c9cea7b0534deb11f,
title = "Multi-modal image registration based on modified-SURF and consensus inliers recovery",
abstract = "Multi-modal image registration has been received significant research attention in past decades. In this paper, we proposed a solution for rigid multi-modal image registration, which focus on handling gradient reversal and region reversal problems happened in multimodal images. We also consider the common property of multi-modal images in geometric structure for feature matching. Besides the improvements in features extraction and matching step, we use a correspondences recovery step to obtain more matches, thus improving the robustness and accuracy of registration. Experiments show that the proposed method is effective.",
author = "Yanjia Chen and Xiuwei Zhang and Fei Li and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71589-6_54",
language = "英语",
isbn = "9783319715889",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "612--622",
editor = "Xiangwei Kong and Yao Zhao and David Taubman",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
}