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Found 2 papers, 1 papers with code
Uncertainty estimation for time series forecasting via Gaussian process regression surrogates
This study proposes a new method for uncertainty estimation based on the surrogate Gaussian process model.
LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection
We point out systematic inconsistencies in the annotations of popular line detection benchmarks - Wireframe and York Urban, carefully reannotate a subset of images and show that (i) existing detectors have improved quality on updated annotations without retraining, suggesting that new annotations correlate better with the notion of correct line segment detection; (ii) the gap between accuracies of our detector and others diminishes to negligible 0. 2 Fh , with our method being the fastest.
Ranked #4 on
Line Segment Detection
on wireframe dataset
(FH metric)
