模板匹配是机器视觉领域的一项共性关键技术，目前基于边缘特征的模板匹配方法存在搜索时间长、在复杂环境下匹配准确率低等问题。为了在保证鲁棒性的同时提升实时性，提出了一种面向边缘特征的实时模板匹配方法。首先，在模板创建阶段，提出了一种新型边缘稀疏方法，通过置信评分机制筛选出模板中不变性强的边缘点，在保留模板关键特征的同时降低模板信息冗余，进而保证稳定性并提升计算效率。其次，在基于金字塔搜索的图像匹配阶段，提出了一种顶层提前筛选方法，采用归一化曼哈顿距离作为限制条件在顶层搜索结果中排除错误目标位姿，以加快后续各层的搜索速度。构建了5种工况不同的数据集，对所提模板匹配方法进行了对比验证，并将其应用于面向自由平面位姿的快速视觉点胶工艺。实验结果表明，所提模板匹配方法在显著提升匹配速度的同时能够保证高准确率，并且能够有效克服光照、旋转、缺陷、多目标、遮挡等干扰因素，满足机器视觉场景中对图像匹配的鲁棒性和实时性要求。

Template matching is a common key technology in the field of machine vision. Currently, edge feature-based template matching methods are facing challenges such as time-consuming searching and low matching accuracy in a complex environment. In order to ensure the robustness while improving the real-time performance, this paper proposed a real-time edge feature-based template matching method. Firstly, in the stage of template creation, a new edge sparse method was proposed, and it can screen out the strong invariant edge points from the template image. It reduces the redundancy of template information while retaining the key template features to ensure the stability and improve the computing efficiency. Secondly, in the stage of pyramid search-based image-matching, a top-level pre-screening method was proposed. Normalized Manhattan distance was used as a constraint to exclude incorrect target poses from the top search results to speed up the search in subsequent layers. Five datasets with different working conditions were constructed, and the proposed template matching method was compared and applied to the fast visual dispensing process for free plane pose. The experimental results show that the proposed matching method can significantly improve the matching speed while ensuring high accuracy. And it can overcome interference factors such as illumination change, rotation, defects, multiple targets, and occlusion, enabling practical applications that require both high robustness and real-time performance.