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Found 5 papers, 4 papers with code
Polygonal Building Extraction by Frame Field Learning
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons.
Input Similarity from the Neural Network Perspective
We first exhibit a multimodal image registration task, for which a neural network trained on a dataset with noisy labels reaches almost perfect accuracy, far beyond noise variance.
Polygonal Building Segmentation by Frame Field Learning
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons.
Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data
In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available.
Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging.
