  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.
