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Local Region Expansion: a Method for Analyzing and Refining Image Matches

机译：局部区域扩展：一种分析和完善图像匹配的方法

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摘要

We present a novel method for locating large amounts of local matches between images, with highly accurate localization. Point matching is one of the most fundamental tasks in computer vision, extensively used in applications such as object detection, object tracking and structure from motion. The major challenge in point matching is to preserve large numbers of accurate matches between corresponding scene locations under different geometric and radiometric conditions, while keeping the number of false positives low. Recent publications have shown that applying an affine transformation model on local regions is a particularly suitable approach for point matching. Yet, affine invariant methods are not used extensively for two reasons: first, because these methods are computationally demanding; and second because the derived affine estimations have limited accuracy. In this work, we propose a novel method of region expansion that enhances region matches detected by any state-of-the-art method. The method is based on accurate estimation of affine transformations, which are used to predict matching locations beyond initially detected matches. We use the improved estimations of affine transformations to locally verify tentative matches in an efficient way. We systematically reject false matches, while improving the localization of correct matches that are usually rejected by state-of-the-art methods.
机译：我们提出了一种新颖的方法，可以在图像之间定位大量的本地匹配，并具有高度精确的定位。点匹配是计算机视觉中最基本的任务之一，广泛用于诸如物体检测，物体跟踪和运动结构之类的应用中。点匹配的主要挑战是在不同的几何和辐射条件下，保持相应场景位置之间的大量精确匹配，同时保持低误报率。最近的出版物表明，在局部区域上应用仿射变换模型是一种特别合适的点匹配方法。然而，仿射不变方法由于以下两个原因而没有得到广泛使用：第一，因为这些方法在计算上有需求。其次，因为导出的仿射估计精度有限。在这项工作中，我们提出了一种新的区域扩展方法，该方法可以增强通过任何最新方法检测到的区域匹配。该方法基于仿射变换的准确估计，该仿射变换用于预测超出初始检测到的匹配的匹配位置。我们使用仿射变换的改进估计以有效的方式本地验证临时匹配。我们系统地拒绝错误匹配，同时改善了正确匹配的定位，而这些匹配通常被最新方法所拒绝。

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