仿射相机图像序列的离群值校正

D. Huynh, R. Hartley, A. Heyden
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引用次数: 37

摘要

众所周知,对于仿射相机模型,形状和运动都可以直接从由图像点坐标构造的所谓图像测量矩阵中分解出来。通过单个SVD操作从该矩阵中提取形状和运动的能力使这种从运动中提取形状的方法具有吸引力;然而,它不能处理缺失的特征点,并且在存在异常值的情况下,对矩阵进行直接奇异值分解会产生高度不可靠的形状和运动分量。在这里,我们提出了一个离群值校正方案,迭代更新图像测量矩阵的元素。每个元素更新的幅度和符号取决于每次迭代中稳健估计的残差。结果是异常值被纠正和保留,从而改善了重建和更小的重投影误差。我们的迭代离群值校正方案已应用于合成和真实视频序列。所得结果非常好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outlier correction in image sequences for the affine camera
It is widely known that, for the affine camera model, both shape and motion can be factorized directly from the so-called image measurement matrix constructed from image point coordinates. The ability to extract both shape and motion from this matrix by a single SVD operation makes this shape-from-motion approach attractive; however, it can not deal with missing feature points and, in the presence of outliers, a direct SVD to the matrix would yield highly unreliable shape and motion components. Here, we present an outlier correction scheme that iteratively updates the elements of the image measurement matrix. The magnitude and sign of the update to each element is dependent upon the residual robustly estimated in each iteration. The result is that outliers are corrected and retained, giving improved reconstruction and smaller reprojection errors. Our iterative outlier correction scheme has been applied to both synthesized and real video sequences. The results obtained are remarkably good.
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