ML Anthology

GLAMpoints: Greedily Learned Accurate Match Points

Prune Truong, Stefanos Apostolopoulos, Agata Mosinska, Samuel Stucky, Carlos Ciller, Sandro De Zanet
ICCV 2019
doi:10.1109/ICCV.2019.01083 /iccv/2019/truong2019iccv-glampoints/

Abstract

We introduce a novel CNN-based feature point detector - Greedily Learned Accurate Match Points (GLAMpoints) - learned in a semi-supervised manner. Our detector extracts repeatable, stable interest points with a dense coverage, specifically designed to maximize the correct matching in a specific domain, which is in contrast to conventional techniques that optimize indirect metrics. In this paper, we apply our method on challenging retinal slitlamp images, for which classical detectors yield unsatisfactory results due to low image quality and insufficient amount of low-level features. We show that GLAMpoints significantly outperforms classical detectors as well as state-of-the-art CNN-based methods in matching and registration quality for retinal images.

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Cite

Text

Truong et al. "GLAMpoints: Greedily Learned Accurate Match Points." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.01083

Markdown

[Truong et al. "GLAMpoints: Greedily Learned Accurate Match Points." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/truong2019iccv-glampoints/) doi:10.1109/ICCV.2019.01083

BibTeX

@inproceedings{truong2019iccv-glampoints,
  title     = {{GLAMpoints: Greedily Learned Accurate Match Points}},
  author    = {Truong, Prune and Apostolopoulos, Stefanos and Mosinska, Agata and Stucky, Samuel and Ciller, Carlos and De Zanet, Sandro},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.01083},
  url       = {https://mlanthology.org/iccv/2019/truong2019iccv-glampoints/}
}