Name
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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Nonlinear system,Pattern recognition,Computer science,Parallel processing,Gaussian,Parametric statistics,Transfer function,Artificial intelligence,User Friendly,Mixture model,Machine learning |
DocType | Volume | Citations |
Journal | abs/1705.06091 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mairéad Grogan | 1 | 0 | 0.68 |
| Rozenn Dahyot | 2 | 340 | 32.62 |