In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.

本文综述了计算机视觉和图像分析中非常重要的模板匹配方法，以及其在不变特征和医学图像分析等领域的应用。此外，讨论了源自经典模板匹配的可变形模型和模板，这些模型在图像配准和深度学习算法中应用广泛。虽然模板匹配具有应用限制，但与其他物体识别方法结合应用能显著提高物体检测和识别的准确率。

图像分析中的模板匹配进展和应用