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Found 9 papers, 3 papers with code
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks
Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential.
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
This can be achieved either by iterative network architectures or by a subsequent projection of the network reconstruction.
Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks
To ensure a structure-preserving mapping from the input to the target domain, existing methods for unpaired image transfer are commonly based on cycle-consistency, causing additional computational resources and instability due to the learning of an inverse mapping.
Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment.
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs
Real-time estimation of actual environment depth is an essential module for various autonomous system tasks such as localization, obstacle detection and pose estimation.
Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks
A novel machine learning framework is proposed that allows prediction of the bearing load curves from RGB images of the liner surface that can be collected with a handheld microscope.
Deep Structure Learning using Feature Extraction in Trained Projection Space
Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data.
Random 2.5D U-net for Fully 3D Segmentation
Convolutional neural networks are state-of-the-art for various segmentation tasks.
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time.
