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Outlier correction in image sequences for the affine camera

机译:仿射照相机图像序列中的异常值校正

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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.
机译:众所周知,对于仿射照相机模型,形状和运动可以直接从图像点坐标构造的所谓的图像测量矩阵分解。通过单个SVD操作从该矩阵中提取形状和运动的能力使得这种形状从运动方法具有吸引力;然而,它不能处理缺失的特征点,并且在异常值存在下,直接SVD到矩阵将产生高度不可靠的形状和运动组件。这里,我们介绍了一个异常校正方案,其迭代地更新图像测量矩阵的元素。对每个元素的更新的幅度和标志取决于每次迭代中的残差估计。结果是校正和保留异常值,从而提高重建和更小的重新注入误差。我们的迭代异常校正方案已应用于合成和真实视频序列。获得的结果非常好。

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