Using unaltered, variable sized and high-resolution images for training fully-convolutional networks

Abstract

Motivated by the usefulness of high resolution images in a broad range of applications, such as medical imaging, astronomy and video surveillance, in this thesis we investigate training a convolutions neural network with unaltered high-resolution images