Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this article, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This “tailored” measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.

中文翻译：

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通过成对仿射估计提高特征匹配精度。

本地图像特征匹配是许多计算机视觉应用程序的核心。当源图像和目标图像之间存在显着的几何差异时，要实现高匹配精度是一项挑战。传统的匹配管道通过引入支撑区域的概念来解决几何差异。在每个特征点周围，支撑区域定义了一个以估计的属性（例如比例，方向，仿射形状等）为特征的相邻区域。要正确分配支撑区域并不是一件容易的事，尤其是在单独处理每个特征时。在本文中，我们建议为每对要比较的特征估计相对仿射变换。这种“量身定做”的几何差异测量更加精确，有助于提高匹配精度。我们的管道可以合并到大多数现有的2D局部图像特征检测器和描述符中。我们对基准数据集的多种选择进行了各种实验，全面评估了其性能。结果表明，大多数经过测试的检测器/描述符与拟议中的流水线都能获得更高的匹配精度。