Optical Flow Estimation is the problem of finding pixel-wise motions between consecutive images.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
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.
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
Optical flow estimation is an essential step for many real-world computer vision tasks.
The goal of this paper is propose a mathematical framework for optical flow refinement with non-quadratic regularization using variational techniques.
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.
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.