Stereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high‐quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature‐based matching techniques to address this gap. Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.

中文翻译：

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功能辅助的立体声相关

数字图像相关（DIC）中的立体声相关涉及对初始猜测敏感的优化问题。实际上，通过在两个立体图像中手动选择一对点来避免该问题，该对点可保证会聚并提供立体映射参数估计值，该估计值用作相邻子集的初始猜测。但是，这种方法并不总是可行的，特别是在存在较大的透视变形的情况下，例如由于大的立体角或样品几何形状的复杂性。因此，希望在整个感兴趣的区域内提供高质量的独立初始估计。最近，SIFT已用于此目的，但当透视变形严重时，它会失败。在这项工作中 我们研究了其他七种基于功能的匹配技术来弥补这一差距。其中，DeepFlow算法可提供最高质量和最空间统一的初始估计。此外，我们将DeepFlow估计值用作常规立体优化中的初始猜测，以计算DIC挑战数据集中样本的几何尺寸。这些几何度量显示出与地面真实情况的极佳一致性，进一步支持了DeepFlow在立体声相关中的选择。