1 research outputs found
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN
Finding a template in a search image is an important task underlying many
computer vision applications. Recent approaches perform template matching in a
feature-space, such as that produced by a convolutional neural network (CNN),
that provides more tolerance to changes in appearance. In this article we
investigate combining features from different layers of a CNN in order to
obtain a feature-space that allows both precise and tolerant template matching.
Furthermore we investigate if enhancing the encoding of shape information by
the CNN can improve the performance of template matching. These investigations
result 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. We further applied the proposed method to
tracking and achieved more robust results.Comment: 17 pages, 5 figures and 5 table