{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robust-registration-of-gaussian-mixtures-for","title":"Robust Registration of Gaussian Mixtures for Colour Transfer","arxiv_id":"1705.06091","date":"2017-05-17","proceeding":null,"authors":["Mairéad Grogan","Rozenn Dahyot"],"abstract":"We present a flexible approach to colour transfer inspired by techniques\nrecently proposed for shape registration. Colour distributions of the palette\nand target images are modelled with Gaussian Mixture Models (GMMs) that are\nrobustly registered to infer a non linear parametric transfer function. We show\nexperimentally that our approach compares well to current techniques both\nquantitatively and qualitatively. Moreover, our technique is computationally\nthe fastest and can take efficient advantage of parallel processing\narchitectures for recolouring images and videos. Our transfer function is\nparametric and hence can be stored in memory for later usage and also combined\nwith other computed transfer functions to create interesting visual effects.\nOverall this paper provides a fast user friendly approach to recolouring of\nimage and video materials.","url_abs":"http://arxiv.org/abs/1705.06091v1","url_pdf":"http://arxiv.org/pdf/1705.06091v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"robust-registration-of-gaussian-mixtures-for","repo_url":"https://github.com/V-Sense/LFToolbox_Recolouring_HPR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}