Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confirm these results we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM.
翻译:在搜索图像中查找模板是许多计算机视觉应用程序的重要任务。 最近的方法在深层地貌空间中执行匹配模板, 由进化神经网络(CNN)生成, 发现它能对外观变化提供更大的容忍度。 在本篇文章中, 我们调查是否加强CNN的形状信息的编码能够产生更显著的特征, 从而改进模板匹配的性能。 本次调查的结果是采用了一种新的模板匹配方法, 在标准基准上产生最先进的结果。 为了确认这些结果, 我们还创建了新的基准, 并显示拟议方法也优于这个新数据集的现有技术。 我们的代码和数据集可以在 https:// github.com/ iminfine/ Dep- DIM 上找到 。