Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in viewpoint, partial occlusion, and non-rigid deformations. This article tests a method of template matching that is more tolerant to such changes in appearance and that can, therefore, more accurately identify image patches. In traditional template matching the comparison between a template and the image is independent of the other templates. In contrast, the method advocated here takes into account the evidence provided by the image for the template at each location and the full range of alternative explanations represented by the same template at other locations and by other templates. Specifically, the proposed method of template matching is performed using a form of probabilistic inference known as "explaining away". The algorithm used to implement explaining away has previously been used to simulate several neurobiological mechanisms, and been applied to image contour detection and pattern recognition tasks. Here it is applied for the first time to image patch matching, and is shown to produce superior results in comparison to the current state-of-the-art methods.

中文翻译：

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解释准确和容错模板匹配中的结果

识别和定位图像块或图像特征集是计算机视觉中许多工作的一项重要任务。传统上这是使用模板匹配来完成的。然而，众所周知，模板匹配在面临由视点变化、部分遮挡和非刚性变形等引起的外观变化时非常脆弱。本文测试了一种模板匹配方法，它更能容忍这种外观变化，因此可以更准确地识别图像块。在传统的模板匹配中，模板和图像之间的比较独立于其他模板。相比之下，这里提倡的方法考虑了每个位置的模板的图像提供的证据，以及其他位置和其他模板的相同模板所代表的全范围替代解释。具体来说，所提出的模板匹配方法是使用一种称为“explaining away”的概率推理形式来执行的。用于实现解释消失的算法以前曾被用于模拟几种神经生物学机制，并应用于图像轮廓检测和模式识别任务。在这里，它首次应用于图像块匹配，与当前最先进的方法相比，它显示出更好的结果。所提出的模板匹配方法是使用一种称为“explaining away”的概率推理形式来执行的。用于实现解释消失的算法以前曾被用于模拟几种神经生物学机制，并应用于图像轮廓检测和模式识别任务。在这里，它首次应用于图像块匹配，与当前最先进的方法相比，它显示出更好的结果。所提出的模板匹配方法是使用一种称为“explaining away”的概率推理形式来执行的。用于实现解释消失的算法以前曾被用于模拟几种神经生物学机制，并应用于图像轮廓检测和模式识别任务。在这里，它首次应用于图像块匹配，与当前最先进的方法相比，它显示出更好的结果。