Template Matching with Deformable Diversity Similarity

Itamar Talmi, Roey Mechrez, Lihi Zelnik-Manor

CVPR 2017

doi:10.1109/CVPR.2017.144 /cvpr/2017/talmi2017cvpr-template/

Abstract

We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.

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Cite

Text

Talmi et al. "Template Matching with Deformable Diversity Similarity." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.144

Markdown

[Talmi et al. "Template Matching with Deformable Diversity Similarity." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/talmi2017cvpr-template/) doi:10.1109/CVPR.2017.144

BibTeX

@inproceedings{talmi2017cvpr-template,
  title     = {{Template Matching with Deformable Diversity Similarity}},
  author    = {Talmi, Itamar and Mechrez, Roey and Zelnik-Manor, Lihi},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.144},
  url       = {https://mlanthology.org/cvpr/2017/talmi2017cvpr-template/}
}