A ROBUST TECHNIQUE FOR MATCHING 2 UNCALIBRATED IMAGES THROUGH THE RECOVERY OF THE UNKNOWN EPIPOLAR GEOMETRY
A ROBUST TECHNIQUE FOR MATCHING 2 UNCALIBRATED IMAGES THROUGH THE RECOVERY OF THE UNKNOWN EPIPOLAR GEOMETRY
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DOI：
10.1016/0004-3702(95)00022-4
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发表时间：
1995-10-01
影响因子：
14.4
通讯作者：
LUONG, QT
中科院分区：
文献类型：
--
作者：
ZHANG, ZY;DERICHE, R;LUONG, QT
本文提出了一种鲁棒的图像匹配方法，利用唯一可用的几何约束，即极面约束。这些图像是未经校准的，即它们之间的运动和相机参数是未知的。因此，图像可以由不同的相机或单个相机在不同的时间瞬间拍摄。如果我们对极外几何进行详尽的搜索，其复杂性是令人望而却步的。我们的方法的基本思想是使用经典技术（在我们的特定实现中使用相关和松弛方法）来查找初始匹配集，然后使用鲁棒技术—最小二乘中值（lmed）—丢弃该集中的错误匹配。然后可以使用有意义的图像准则准确地估计极几何形状。像在立体匹配中一样，通过使用恢复的极层几何图形，最终找到更多的匹配。进行了大量的实验，取得了很好的效果。关于松弛技术，我们定义了一种新的匹配支持度量，它允许更高的变形容忍相对于图像平面上的刚性变换，并且远匹配比近匹配的贡献更小。提出了一种新的匹配更新策略，即只选择匹配支持度高、匹配模糊度低的匹配。这种更新策略不同于经典的“赢者通吃”策略，后者很容易陷入局部最小值，也不同于“输者一无所有”策略，后者通常非常缓慢。该算法已经过广泛的测试，在具有许多重复模式的场景中效果显著。
This paper proposes a robust approach to image matching by exploiting the only available geometric constraint, namely, the epipolar constraint. The images are uncalibrated, namely the motion between them and the camera parameters are not known. Thus, the images can be taken by different cameras or a single camera at different time instants. If we make an exhaustive search for the epipolar geometry, the complexity is prohibitively high. The idea underlying our approach is to use classical techniques (correlation and relaxation methods in our particular implementation) to find an initial set of matches, and then use a robust technique-the Least Median of Squares (LMedS)-to discard false matches in this set. The epipolar geometry can then be accurately estimated using a meaningful image criterion. More matches are eventually found, as in stereo matching, by using the recovered epipolar geometry. A large number of experiments have been carried out, and very good results have been obtained.Regarding the relaxation technique, we define a new measure of matching support, which allows a higher tolerance to deformation with respect to rigid transformations in the image plane and a smaller contribution for distant matches than for nearby ones. A new strategy for updating matches is developed, which only selects those matches having both high matching support and low matching ambiguity. The update strategy is different from the classical ''winner-take-all'', which is easily stuck at a local minimum, and also from ''loser-take-nothing'', which is usually very slow. The proposed algorithm has been widely tested and works remarkably well in a scene with many repetitive patterns.