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Coarse-to-fine region selection and matching

机译:粗到细区域选择和匹配

摘要

We present a new approach to wide baseline matching. We propose to use a hierarchical decomposition of the image domain and coarse-to-fine selection of regions to match. In contrast to interest point matching methods, which sample salient regions to reduce the cost of comparing all regions in two images, our method eliminates regions systematically to achieve efficiency. One advantage of our approach is that it is not restricted to covariant salient regions, which is too restrictive under large viewpoint and leads to few corresponding regions. Affine invariant matching of regions in the hierarchy is achieved efficiently by a coarse-to-fine search of the affine space. Experiments on two benchmark datasets shows that our method finds more correct correspondence of the image (with fewer false alarms) than other wide baseline methods on large viewpoint change. © 2015 IEEE.
机译:我们提出了一种广泛的基线匹配的新方法。我们建议使用图像域的分层分解以及要匹配的区域的粗略选择。与兴趣点匹配方法(对显着区域进行采样以减少比较两个图像中所有区域的成本)相反,我们的方法系统地消除了区域以实现效率。我们方法的优点之一是它不限于协变显着区域,这在大视野下过于严格,导致相应区域很少。通过对仿射空间进行从粗到精的搜索,可以有效地实现层次结构中区域的仿射不变匹配。在两个基准数据集上进行的实验表明,与其他在较大视点变化时使用的宽基线方法相比,我们的方法可以找到更正确的图像对应关系(错误警报更少)。 ©2015 IEEE。

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