4 papers
Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels
Juan Miguel Valverde, Dim P. Papadopoulos, Rasmus Larsen +1
Standard deep learning models for image segmentation cannot guarantee topology accuracy, failing to preserve the correct number of connected components or structures. This, in turn…
MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose +2
Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textua…
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image Segmentation
Juan Miguel Valverde, Maja Østergaard, Adrian Rodriguez-Palomo +4
Accurate segmentation of thin, tubular structures (e.g., blood vessels) is challenging for deep neural networks. These networks classify individual pixels, and even minor misclassi…
TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy
Juan Miguel Valverde, Motoya Koga, Nijihiko Otsuka +1
We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions…