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Found 11 papers, 4 papers with code
Visual Analysis of Multi-outcome Causal Graphs
We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs.
A Plug-and-Play Untrained Neural Network for Full Waveform Inversion in Reconstructing Sound Speed Images of Ultrasound Computed Tomography
To the best of our knowledge, this study represents the first attempt to propose an implicit UNN for FWI in reconstructing sound speed images for USCT.
Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection
First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images.
Advances and Challenges in Multimodal Remote Sensing Image Registration
Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e. g., optical sensors) to the new generation of multimodal sensors [e. g., multispectral, hyperspectral, light detection and ranging (LiDAR) and synthetic aperture radar (SAR) sensors].
Implicit Multidimensional Projection of Local Subspaces
We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation.
Fast and Robust Registration of Aerial Images and LiDAR data Based on Structrual Features and 3D Phase Correlation
Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT).
Generalized Energy Based Models
We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base.
Cost and Actual Causation
I propose the purpose our concept of actual causation serves is minimizing various cost in intervention practice.
