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Found 7 papers, 3 papers with code
Towards Reliable Evaluation of Algorithms for Road Network Reconstruction from Aerial Images
Existing connectivity-oriented performance measures rank road delineation algorithms inconsistently, which makes it difficult to decide which one is best for a given application.
Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks.
Persistent Homology with Improved Locality Information for more Effective Delineation
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results.
Promoting Connectivity of Network-Like Structures by Enforcing Region Separation
The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image.
Towards Reliable Evaluation of Road Network Reconstructions
Existing performance measures rank delineation algorithms inconsistently, which makes it difficult to decide which one is best in any given situation.
Tracing in 2D to Reduce the Annotation Effort for 3D Deep Delineation
The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis.
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
We propose a method for semi-supervised training of structured-output neural networks.
