Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method which efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with $k$ features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.

翻译：模板匹配是计算机视觉中的一个基本问题，广泛应用于目标检测、图像配准和目标跟踪等领域。当前方法依赖最近邻匹配，通过将查询特征空间中每个像素用其在模板中的最近邻表示，将查询特征空间转换为最近邻空间。基于最近邻的方法在遮挡、外观变化和非刚性变换场景下表现更优；然而，它们在高分辨率数据和高维特征下扩展性较差。我们提出一种基于最近邻的方法，该方法有效减少了最近邻计算量，并在最近邻场中引入滤波。在最近邻计算之前引入向量量化步骤，用$k$个特征表示模板，并利用最近邻场上的滤波响应来比较模板与查询在特征上的分布。我们证明，该方法在低分辨率数据上实现了最先进的性能，并且在更高分辨率下优于先前方法。