Template matching is a fundamental problem in computer vision and has applications in various fields, such as object detection, image registration, and object tracking. The current state-of-the-art methods rely on nearest-neighbour (NN) matching in which the query feature space is converted to NN space by representing each query pixel with its NN in the template pixels. The NN-based methods have been shown to perform better in occlusions, changes in appearance, illumination variations, and non-rigid transformations. However, NN matching scales poorly with high-resolution data and high feature dimensions. In this work, we present an NN-based template-matching method which efficiently reduces the NN computations and introduces filtering in the NN fields to consider deformations. A vector quantization step first represents the template with $k$ features, then filtering compares the template and query distributions over the $k$ features. We show that state-of-the-art performance was achieved in low-resolution data, and our method outperforms previous methods at higher resolution showing the robustness and scalability of the approach.

翻译：模板匹配是计算机视觉中的基本问题，在目标检测、图像配准和目标跟踪等领域具有广泛应用。当前最先进的方法依赖于最近邻匹配，其通过将查询特征空间转换为最近邻空间：将每个查询像素表示为模板像素中的最近邻点。基于最近邻的方法在遮挡、外观变化、光照变化及非刚性变换中表现更优。然而，最近邻匹配在高分辨率数据和高特征维度下扩展性较差。本文提出一种基于最近邻的模板匹配方法，该方法高效减少了最近邻计算量，并在最近邻场中引入滤波以考虑形变。首先通过矢量量化步骤将模板表示为$k$个特征，随后通过滤波比较模板与查询在$k$个特征上的分布。实验表明，在低分辨率数据中该方法达到了最先进的性能，而在更高分辨率下，该方法优于先前方法，展现了其鲁棒性与可扩展性。