{"id":24968182,"url":"https://github.com/sfu-cs-vision-lab/tps","last_synced_at":"2025-03-29T03:23:52.048Z","repository":{"id":271817289,"uuid":"914660576","full_name":"sfu-cs-vision-lab/tps","owner":"sfu-cs-vision-lab","description":"Thin-Plate Spline Illumination Estimation Automatic White Balancing Method","archived":false,"fork":false,"pushed_at":"2025-01-16T20:14:09.000Z","size":477,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-02-03T13:16:52.281Z","etag":null,"topics":["camera-calibration","color","color-constancy","color-measurement","color-vision","digital-image-processing","illumination","image-registration","interpolation","light-sources","neural-networks","vision","visual-optics"],"latest_commit_sha":null,"homepage":"https://doi.org/10.1364/JOSAA.28.000940","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sfu-cs-vision-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-10T03:34:59.000Z","updated_at":"2025-01-17T01:17:58.000Z","dependencies_parsed_at":"2025-01-10T04:26:04.456Z","dependency_job_id":"8fad4138-0e5b-4882-8007-38ab8a070ef1","html_url":"https://github.com/sfu-cs-vision-lab/tps","commit_stats":null,"previous_names":["sfu-cs-vision-lab/tps"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfu-cs-vision-lab%2Ftps","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfu-cs-vision-lab%2Ftps/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfu-cs-vision-lab%2Ftps/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfu-cs-vision-lab%2Ftps/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sfu-cs-vision-lab","download_url":"https://codeload.github.com/sfu-cs-vision-lab/tps/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246132606,"owners_count":20728520,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["camera-calibration","color","color-constancy","color-measurement","color-vision","digital-image-processing","illumination","image-registration","interpolation","light-sources","neural-networks","vision","visual-optics"],"created_at":"2025-02-03T13:16:58.602Z","updated_at":"2025-03-29T03:23:52.028Z","avatar_url":"https://github.com/sfu-cs-vision-lab.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Thin-Plate Spline Illumination Estimation Automatic White Balancing Method\n\n[![Open in MATLAB Online](https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg)](https://matlab.mathworks.com/open/github/v1?repo=sfu-cs-vision-lab/tps)\n\n### Abstract\nThin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.\n\n### Citation\n\n```\n@article{Shi:11,\n    author = {Lilong Shi and Weihua Xiong and Brian Funt},\n    journal = {J. Opt. Soc. Am. A},\n    keywords = {Digital image processing; Vision, color, and visual optics ; Color; Color, measurement ; Color vision; Camera calibration; Illumination; Image registration; Interpolation; Light sources; Neural networks},\n    number = {5},\n    pages = {940--948},\n    publisher = {Optica Publishing Group},\n    title = {Illumination estimation via thin-plate spline interpolation},\n    volume = {28},\n    month = {May},\n    year = {2011},\n    url = {https://opg.optica.org/josaa/abstract.cfm?URI=josaa-28-5-940},\n    doi = {10.1364/JOSAA.28.000940},\n    abstract = {Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfu-cs-vision-lab%2Ftps","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsfu-cs-vision-lab%2Ftps","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfu-cs-vision-lab%2Ftps/lists"}