# GLAMpoints: Greedily Learned Accurate Match points

## TL;DR

GLAMpoints is a CNN-based feature detector trained semi-supervised to produce highly repeatable interest points, significantly improving matching and registration in retinal images and adaptable to other domains.

## Contribution

Introduces a novel semi-supervised CNN detector, GLAMpoints, optimized for domain-specific matching accuracy, outperforming classical and existing CNN methods.

## Key findings

- Outperforms classical detectors in retinal image matching
- Achieves higher registration accuracy in challenging images
- Can be extended to natural image domains

## Abstract

We introduce a novel CNN-based feature point detector - 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. Our method can also be extended to other domains, such as natural images. Training code and model weights are available at https://github.com/PruneTruong/GLAMpoints_pytorch.