兰萨克
离群值
仿射变换
计算机科学
算法
图形
单应性
基本矩阵(线性微分方程)
数学
人工智能
图像(数学)
理论计算机科学
统计
射影空间
数学分析
投射试验
纯数学
作者
Dániel Baráth,Jǐŕı Matas
标识
DOI:10.1109/cvpr.2018.00704
摘要
A novel method for robust estimation, called Graph-Cut RANSAC1, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard CPU).
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