{"id":"W2107558608","doi":"10.1109/tip.2002.802531","title":"A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":440,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Color constancy; Standard illuminant; Computer science; Algorithm; Gamut; Color balance; Artificial intelligence; Invariant (physics); ICC profile; Color space; Computer vision; Color model; Color image; Image processing; Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001005135,0.00009689259,0.0001924175,0.00006013188,0.0002529114,0.00004776951,0.0001593149,0.00001861979,0.0002133187],"category_scores_gemma":[0.000001575062,0.00008507246,0.00001781596,0.0002074002,0.0002507003,0.0002890686,0.000002983042,0.00009818812,0.000005800672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007895713,"about_ca_system_score_gemma":0.00003983456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017502,"about_ca_topic_score_gemma":0.000002626963,"domain_scores_codex":[0.9992439,0.00003954289,0.0001954048,0.0002670719,0.000128225,0.0001258767],"domain_scores_gemma":[0.9994001,0.0001876127,0.000113354,0.0001790226,0.0000729623,0.0000469863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008834689,0.001677345,0.0008642074,0.00005799413,0.0001180832,0.000001257274,0.003633551,0.005257657,0.02591318,0.0003848676,0.0001644962,0.961839],"study_design_scores_gemma":[0.001435428,0.0001727054,0.0002263336,0.000108314,0.0001396418,0.000008240095,0.003842334,0.8643599,0.1282396,0.0008895708,0.000264299,0.0003136275],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04003079,0.00008218462,0.9587382,0.0001707256,0.00002021377,0.0001572618,0.00007626978,0.00002147515,0.0007028583],"genre_scores_gemma":[0.8155816,0.000001440189,0.1842976,0.00001654742,0.000009949279,0.00003981256,0.000005392835,0.000006516532,0.00004118843],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9615254,"threshold_uncertainty_score":0.3469154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468893909024121,"score_gpt":0.3963490003216183,"score_spread":0.2494596094192062,"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."}}