A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection

Yizhen Lao, Omar Ait-Aider
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引用次数: 39

Abstract

We present a robust method which compensates RS distortions in a single image using a set of image curves, basing on the knowledge that they correspond to 3D straight lines. Unlike in existing work, no a priori knowledge about the line directions (e.g. Manhattan World assumption) is required. We first formulate a parametric equation for the projection of a 3D straight line viewed by a moving rolling shutter camera under a uniform motion model. Then we propose a method which efficiently estimates ego angular velocity separately from pose parameters, using at least 4 image curves. Moreover, we propose for the first time a RANSAC-like strategy to select image curves which really correspond to 3D straight lines and reject those corresponding to actual curves in 3D world. A comparative experimental study with both synthetic and real data from famous benchmarks shows that the proposed method outperforms all the existing techniques from the state-of-the-art.
一种基于自动特征选择的强滚动快门效果校正方法
我们提出了一种鲁棒的方法,该方法使用一组图像曲线来补偿单个图像中的RS失真,基于它们对应于3D直线的知识。与现有工作不同,不需要关于线方向的先验知识(例如曼哈顿世界假设)。我们首先建立了均匀运动模型下运动卷帘式相机所观察到的三维直线投影的参数方程。然后,我们提出了一种至少使用4条图像曲线独立于姿态参数有效估计自我角速度的方法。此外,我们首次提出了一种类似ransac的策略来选择真正对应于三维直线的图像曲线,并拒绝对应于三维世界中实际曲线的图像曲线。与著名基准测试的合成数据和真实数据进行对比实验研究表明,该方法优于现有的所有最新技术。
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