{"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/linear-colour-segmentation-revisited","title":"Linear colour segmentation revisited","arxiv_id":"1901.00534","date":"2019-01-02","proceeding":null,"authors":["Anna Smagina","Valentina Bozhkova","Sergey Gladilin","Dmitry Nikolaev"],"abstract":"In this work we discuss the known algorithms for linear colour segmentation\nbased on a physical approach and propose a new modification of segmentation\nalgorithm. This algorithm is based on a region adjacency graph framework\nwithout a pre-segmentation stage. Proposed edge weight functions are defined\nfrom linear image model with normal noise. The colour space projective\ntransform is introduced as a novel pre-processing technique for better handling\nof shadow and highlight areas. The resulting algorithm is tested on a benchmark\ndataset consisting of the images of 19 natural scenes selected from the\nBarnard's DXC-930 SFU dataset and 12 natural scene images newly published for\ncommon use. The dataset is provided with pixel-by-pixel ground truth colour\nsegmentation for every image. Using this dataset, we show that the proposed\nalgorithm modifications lead to qualitative advantages over other model-based\nsegmentation algorithms, and also show the positive effect of each proposed\nmodification. The source code and datasets for this work are available for free\naccess at http://github.com/visillect/segmentation.","url_abs":"http://arxiv.org/abs/1901.00534v1","url_pdf":"http://arxiv.org/pdf/1901.00534v1.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":"linear-colour-segmentation-revisited","repo_url":"https://github.com/visillect/colorsegdataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"linear-colour-segmentation-revisited","repo_url":"https://github.com/visillect/segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}