We present a feature-based image registration method, the stepwise image registration (SIR), with a closed-form solution. Our SIR creates an inlier pool and a candidate pool as the initialization, and then gradually enriches the inlier pool and refines the transformation. In each step, the enriched correspondence exclusively tunes the transformation coefficient within the confirmed inlier pairs, instead of updating the mapping using the complete putative set. In turn, the refined transformation prunes inconsistent mismatches to alleviate the incoming matching ambiguity. The context-aware locality measure (CALM) is designed for dissimilarity measure. The capability of the CALM can be enhanced by the progressive inlier pool enrichment. Finally, a retrieval process is performed based on the finest CALM and alignment, by which the inlier pool is maximized. Extensive experiments of enrichment evaluation, feature matching, image registration, and image retrieval demonstrate the favorable performance of our SIR against state-of-the-art methods. The code and datasets are available at https://github.com/sucv/SIR.

中文翻译：

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用于逐步图像配准的内部池富集的上下文感知局部性度量

我们提出一种基于特征的图像配准方法，即逐步图像配准（SIR），并采用封闭形式的解决方案。我们的SIR创建一个内部池和一个候选池作为初始化，然后逐渐丰富内部池并优化转换。在每个步骤中，丰富的对应关系仅在已确认的对对内调整转换系数，而不是使用完整的推定集更新映射。反过来，精细的转换会修剪不一致的不匹配项，以减轻传入的匹配歧义。上下文感知位置度量（CALM）设计用于差异度量。可通过逐步的内部池富集来增强CALM的功能。最后，根据最佳的CALM和对齐方式执行检索过程，从而使内部池最大化。丰富的评估，特征匹配，图像配准和图像检索的广泛实验证明了我们的SIR相对于最新方法的良好性能。代码和数据集可从https://github.com/sucv/SIR获得。