ARSAC: Robust model estimation via adaptively ranked sample consensus

Rui Li, Jinqiu Sun, Yu Zhu, Haisen Li, Yanning Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

RANSAC is a popular model estimation algorithm in various of computer vision applications. However, it easily gets slow as the inlier rate of the measurements declines. In this paper, a novel Adaptively Ranked Sample Consensus (ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. Our algorithm adopts non-uniform sampling based on the ranked measurements. We propose an adaptive scheme which updates the ranking of the measurements on each trial, to incorporate high quality measurement into sample at high priority. We also design a geometric constraint during sampling process, which could alleviate degenerate cases caused by non-uniform sampling in epipolar geometry. Experiments on real-world data demonstrate the effectiveness and robustness of the proposed method compared to the state-of-the-art methods.

源语言英语
主期刊名Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings
编辑Liang Wang, Xiang Bai, Jinfeng Yang, Qingshan Liu, Deyu Meng, Qinghua Hu, Ming-Ming Cheng
出版商Springer Verlag
591-602
页数12
ISBN(印刷版)9789811073014
DOI
出版状态已出版 - 2017
活动2nd Chinese Conference on Computer Vision, CCCV 2017 - Tianjin, 中国
期限: 11 10月 201714 10月 2017

出版系列

姓名Communications in Computer and Information Science
772
ISSN(印刷版)1865-0929

会议

会议2nd Chinese Conference on Computer Vision, CCCV 2017
国家/地区中国
Tianjin
时期11/10/1714/10/17

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