运动约束模式

Cornelia Fermuller
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引用次数: 3

摘要

使用局部图像运动作为输入来处理自运动恢复问题,已发表的算法利用二维局部图像运动(光流,对应,图像流的导数)与三维运动和结构相关的几何约束。由于事实证明实现精确输入(局部图像运动)非常困难,大量的努力已经投入到鲁棒技术的发展。提出了一种基于全局约束的自我运动估计问题的新方法。证明了局部法向流测量在图像平面上形成了全局模式。这些图案的位置与三维运动参数有关。通过定位这些只依赖于运动参数子集的模式,通过简单的搜索技术,可以找到三维运动参数。所提出的算法程序具有很强的鲁棒性,因为它不受正常流量测量中的小扰动的影响。事实上,由于只使用了正常流量测量的符号,因此在图像测量中可以估计平移方向和旋转轴,误差高达100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion constraint patterns
The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.<>
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