{"id":"W1988165713","doi":"10.1364/josaa.28.000940","title":"Illumination estimation via thin-plate spline interpolation","year":2011,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; Samsung Advanced Institute of Technology","keywords":"Artificial intelligence; Computer vision; Thin plate spline; Chromaticity; Computer science; Spline (mechanical); Computation; Global illumination; Demosaicing; ICC profile; Spline interpolation; Interpolation (computer graphics); Mathematics; Rendering (computer graphics); Image processing; Image (mathematics); Color image; Algorithm; Bilinear interpolation; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033659,0.0007741529,0.0009786222,0.001104485,0.0004818977,0.0009471547,0.001117453,0.0006939545,0.002844289],"category_scores_gemma":[0.003610741,0.0005506696,0.001144557,0.001252321,0.0004278407,0.0007600091,0.0008908407,0.001884692,0.001355083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004416146,"about_ca_system_score_gemma":0.001118024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0039685,"about_ca_topic_score_gemma":0.003714938,"domain_scores_codex":[0.9995083,0.0001099539,0.00002387221,0.00009355579,0.0002146251,0.00004983308],"domain_scores_gemma":[0.9986016,0.0005385505,0.000126195,0.0002343657,0.0004577604,0.00004152101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000317913,0.0001063922,0.001720693,0.0001856747,0.00007357921,0.0001275574,0.0001889973,0.5711887,0.05233423,0.00612855,0.001774687,0.3658531],"study_design_scores_gemma":[0.000004351008,0.00001299028,0.0002031559,0.000005746722,0.000004329278,0.00002428492,0.00000851376,0.9909067,0.007460509,0.0008289629,0.0005298815,0.00001051646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007732347,0.00002817696,0.9912524,0.00001304119,0.000009433762,0.00001338961,0.00002357796,0.0005884291,0.0003391726],"genre_scores_gemma":[0.1360904,0.000124099,0.8616229,0.00001464135,0.00001496847,0.00005839245,0.0002829461,0.0002385266,0.001553032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0039685,"threshold_uncertainty_score":0.009515166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267030147033775,"score_gpt":0.2572526660794225,"score_spread":0.2445823646090847,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}