a) Input data, b) segmentation result for fixed-stride training data, c) segmentation result for object-based training data, d) segmentation result for training data generated by object-based positioning + object integrity constraint. Yellow = true positive, red = false negative, green = false positive.</p
Machine learning requires a human description of the data. The manual dataset description is very ti...
Neural networks are a large number of interconnected mathematical neural models. Neural networks are...
The distributions of predictions scores are visualized using kernel density estimate over pose predi...
Black arrows indicate substantial changes in comparison to Fig 9 (prediction score threshold of 0.9)...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Effects of tiling method, training data set size and prediction threshold on the segmentation perfor...
Black arrows indicate substantial changes in comparison to Fig 10 (same prediction score but Mask R-...
Detection performance of the trained Mask R-CNN model on each validation subset VΓ.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
The results here are on the validation set. Recurrent CNNs (a-d) were used as backbones in Faster R-...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
We present a method for training CNN-based object class detectors directly using mean average precis...
This vector file (in zipped shapefile format) is the results of the Mask-CNN analysis. For additio...
Machine learning requires a human description of the data. The manual dataset description is very ti...
Neural networks are a large number of interconnected mathematical neural models. Neural networks are...
The distributions of predictions scores are visualized using kernel density estimate over pose predi...
Black arrows indicate substantial changes in comparison to Fig 9 (prediction score threshold of 0.9)...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Blue arrows indicate shift from fixed-stride to object-based tile positioning, green arrows indicate...
Effects of tiling method, training data set size and prediction threshold on the segmentation perfor...
Black arrows indicate substantial changes in comparison to Fig 10 (same prediction score but Mask R-...
Detection performance of the trained Mask R-CNN model on each validation subset VΓ.</p
CNN segmentation performance metrics for training, validation, and test sets.</p
The results here are on the validation set. Recurrent CNNs (a-d) were used as backbones in Faster R-...
We show here the validation loss (normalized from 0 to 1) as a function of epoch number throughout C...
We present a method for training CNN-based object class detectors directly using mean average precis...
This vector file (in zipped shapefile format) is the results of the Mask-CNN analysis. For additio...
Machine learning requires a human description of the data. The manual dataset description is very ti...
Neural networks are a large number of interconnected mathematical neural models. Neural networks are...
The distributions of predictions scores are visualized using kernel density estimate over pose predi...