论文标题
通过分层卷积特征与形状有偏见的CNN匹配的强大模板匹配
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN
论文作者
论文摘要
在搜索图像中找到模板是许多计算机视觉应用程序的重要任务。最近的方法在由卷积神经网络（CNN）产生的深处空间中执行模板匹配，该卷积神经网络（CNN）可提供对外观变化的更宽容。在本文中，我们调查了增强CNN的形状信息编码是否会产生更明显的功能，从而改善模板匹配的性能。这项研究导致了一种新的模板匹配方法，该方法在标准基准下产生最先进的结果。为了确认这些结果，我们还创建了一个新的基准测试标准，并表明所提出的方法还优于此新数据集中的现有技术。我们的代码和数据集可在以下网址找到：https：//github.com/iminfine/deep-dim。
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.
