1 citations · 1 across the 2 of their papers we have counts for
4 papers
Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation
Julien Walther, Rémi Giraud, Michaël Clément
Superpixels are widely used in computer vision to simplify image representation and reduce computational complexity. While traditional methods rely on low-level features, deep lear…
Superpixel Segmentation: A Long-Lasting Ill-Posed Problem
Rémi Giraud, Michaël Clément
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such co…
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…