GLAMpoints: Greedily Learned Accurate Match Points

Prune Truong, Stefanos Apostolopoulos, Agata Mosinska, Samuel Stucky, Carlos Ciller, Sandro De Zanet; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 10732-10741

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.

Related Material


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[bibtex]
@InProceedings{Truong_2019_ICCV,
author = {Truong, Prune and Apostolopoulos, Stefanos and Mosinska, Agata and Stucky, Samuel and Ciller, Carlos and Zanet, Sandro De},
title = {GLAMpoints: Greedily Learned Accurate Match Points},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}