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 whether enhancing the CNN’s encoding of shape information can produce more distinguishable features, so as to improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results in 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.
|Number of pages||12|
|Publication status||Published - 1 Jan 2022|
|Event||International Conference on Image, Vision and Intelligent Systems - |
Duration: 19 Jun 2021 → 20 Jun 2021
|Conference||International Conference on Image, Vision and Intelligent Systems|
|Period||19/06/2021 → 20/06/2021|