Computer Vision News Computer Vision News 10 What is image-to-graph transformation? Image-to-graph transformation means that we are having an image as input and we want to extract a graph from that image and that graph should represent a physical structure inside of the image. There are many downstream applications that these structural graphs can be used for. For example, if we are in the domain of remote sensing images, then we can extract road networks as graphs and these graphs can be used for example for navigation and other fields; in the medical imaging field it can represent vascular structures, for example of the retina or of the brain by using a graph - and then the graph can be used for brain analysis or also for diagnosis of diseases. The field of image-to-graph extraction is relatively small, so there's not much research in it. The merit of this work is that it tries to tackle the data scarcity problem, since it's very hard to annotate those graph labels. Was this the biggest challenge of this work? There was an even bigger one. The work wants to transfer knowledge between two domains, therefore utilize data in one domain to train the network in another domain. The biggest challenge there is the large domain gap between those two domains, because we are dealing with satellite images on the one hand and medical scans on the other hand, and there's a large difference between those images. Alexander Berger is a PhD student with Johannes Paetzold (at the right of the poster) and Daniel Rueckert at Technical University Munich. Alex has presented an excellent paper at WACV 2025, which has deserved an honorable mention for Best Student Paper award. Best Student Paper Hon. Men.
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