About
Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
Source: Devon: Deformable Volume Network for Learning Optical Flow
Benchmarks
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Datasets
Latest papers without code
Learning for Unconstrained Space-Time Video Super-Resolution
Recent years have seen considerable research activities devoted to video enhancement that simultaneously increases temporal frame rate and spatial resolution.
OPTICAL FLOW ESTIMATION VIDEO ENHANCEMENT VIDEO SUPER-RESOLUTION
Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep Learning
We estimated the velocity vector field from the local estimation of the blur model parameters using an deep neural network and achieved a prediction with a regression coefficient of 0. 92 between the ground truth simulated vector field and the output of the network.
Analysis of Latent-Space Motion for Collaborative Intelligence
When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model.
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
IMAGE RESTORATION MULTI-FRAME SUPER-RESOLUTION OPTICAL FLOW ESTIMATION
Deep Online Fused Video Stabilization
The network fuses optical flow with real/virtual camera pose histories into a joint motion representation.
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
Optical flow estimation is an essential step for many real-world computer vision tasks.
Nonlinear Evolutionary PDE-Based Refinement of Optical Flow
The goal of this paper is propose a mathematical framework for optical flow refinement with non-quadratic regularization using variational techniques.
On the Space-Time Statistics of Motion Pictures
It is well-known that natural images possess statistical regularities that can be captured by bandpass decomposition and divisive normalization processes that approximate early neural processing in the human visual system.
Neural Particle Image Velocimetry
In this work, we introduce a convolutional neural network adapted to the problem, namely Volumetric Correspondence Network (VCN) which was recently proposed for the end-to-end optical flow estimation in computer vision.
FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
The photometric loss minimizes pixel intensity differences differences, the smoothness loss encourages similar magnitudes between neighbouring vectors, and a correlation loss that is used to maintain the intensity similarity between fixed and moving image slices.
IMAGE REGISTRATION MEDICAL IMAGE REGISTRATION OPTICAL FLOW ESTIMATION