A Robust Method for Estimating the Fundamental Matrix

机译：估计基本矩阵的稳健方法

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In this paper, we propose a robust method to estimate the fundamental matrix in the presence of outliers. The new method uses random minimum subsets as a search engine to find inliers. The fundamental matrix is computed from a minimum subset and subsequently evaluated over the entire data set by means of the same measure, namely minimization of 2D reprojection error. A mixture model of Gaussian and Uniform distributions is used to describe the image errors. An iterative algorithm is developed for estimating the outlier percentage and noise level in the mixture model. Simulation results are provided to illustrate the performance of the proposed method.

机译：在本文中，我们提出了一种鲁棒的方法来估计存在异常值的基本矩阵。新方法使用随机的最小子集作为搜索引擎来查找内点。基本矩阵是从最小子集计算得出的，然后通过相同的方法对整个数据集进行评估，即最小化2D重投影误差。高斯分布和均匀分布的混合模型用于描述图像误差。开发了一种迭代算法来估计混合模型中的异常值百分比和噪声水平。仿真结果说明了该方法的性能。

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《Biennial Australian Pattern Recognition Society Conference(DICTA2003) v.2; 2003; Sydney; AU》|2003年|P.633-642|共10页
会议地点 Sydney(AU);Sydney(AU);Sydney(AU)
作者
C.L. Feng; Y.S. Hung;
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作者单位

Department of Electrical and Electric Engineering The University of Hong Kong Pokfulam Road, Hong Kong;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
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A Robust Method for Estimating the Fundamental Matrix

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