@techreport{DBLP:journals/corr/GroganD17, 
author =  {Mair{\'{e}}ad Grogan and Rozenn Dahyot}, 
title =  {Robust Registration of Gaussian Mixtures for Colour Transfer},
institution =  {Trinity College Dublin Ireland},
abstract = {We present a flexible approach to colour transfer inspired by techniques 
recently proposed for shape registration. Colour distributions of the palette and target 
images are modelled with Gaussian Mixture Models (GMMs) that are robustly registered to 
infer a non linear parametric transfer function. We show experimentally that our approach compares 
well to current techniques both quantitatively and qualitatively. Moreover, our technique is 
computationally the fastest and can take efficient advantage of parallel processing architectures 
for recolouring images and videos. Our transfer function is parametric and hence can be stored in memory 
for later usage and also combined with other computed transfer functions to create interesting visual effects. 
Overall this paper provides a fast user friendly approach to recolouring of image and video materials.},
volume =  {abs/1705.06091}, 
year =  {2017}, 
url =  {https://arxiv.org/pdf/1705.06091.pdf}, 
archivePrefix =  {arXiv}, 
eprint =  {1705.06091},
timestamp =  {Wed, 07 Jun 2017 14:41:30 +0200} }

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