The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields
The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields
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DOI：
10.1006/cviu.1996.0006
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发表时间：
1996-01-01
影响因子：
4.5
通讯作者：
Anandan, P
中科院分区：
文献类型：
--
作者：
Black, MJ;Anandan, P
用于估计光流的大多数方法假设在有限图像区域内仅存在单个运动。在涉及透明度、深度不连续性、独立移动对象、阴影和镜面反射的常见情况下，违反了这种单一运动假设。为了鲁棒地估计光流，必须放松单一运动假设。本文提出了一个框架的基础上强大的估计，解决违反亮度恒定性和空间平滑性的假设所造成的多个运动。我们展示了如何强大的估计框架可以应用到光流问题的标准配方，从而降低其敏感性，违反其基本假设，该方法已被应用到三个标准的技术恢复光流：基于区域的回归，相关性和运动不连续性的正则化。本文重点研究了区域内多参数运动模型的恢复，以及分段光滑流场的恢复，并提供了自然和合成图像序列的例子。(C)出版社：Academic Press，Inc.
Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. This single motion assumption is violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This paper presents a framework based on robust estimation that addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how the robust estimation framework can be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions, The approach has been applied to three standard techniques for recovering optical flow: area-based regression, correlation, and regularization with motion discontinuities. This paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewise-smooth flow fields, and provides examples with natural and synthetic image sequences. (C) 1996 Academic Press, Inc.