{"id":"W2218132232","doi":"10.5539/mas.v9n13p140","title":"Color Balance for Panoramic Images","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"People's Government of Jilin Province; Natural Science Foundation of Jilin Province","keywords":"Artificial intelligence; Computer vision; Image stitching; Color balance; Normalization (sociology); Pixel; Color correction; Computer science; Color image; Feature (linguistics); Image (mathematics); Mathematics; Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006458224,0.0001259341,0.0001382514,0.0001093328,0.0002834378,0.0003341927,0.001698367,0.00002186111,0.000001289545],"category_scores_gemma":[0.0000979974,0.0001107521,0.0000277639,0.0006310085,0.0003315532,0.0008985872,0.0004017212,0.00007726481,0.00006913541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009480822,"about_ca_system_score_gemma":0.0002799044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001962322,"about_ca_topic_score_gemma":4.62321e-7,"domain_scores_codex":[0.9982119,0.000006872477,0.0001563685,0.0006457338,0.000489403,0.0004896877],"domain_scores_gemma":[0.9988655,0.00005123423,0.00007428488,0.0005836172,0.000184607,0.0002406832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002068028,0.00005758294,0.00007427215,0.000008406045,0.000001793305,0.000002558246,0.0010089,0.001067353,0.6922482,0.0793061,0.002097705,0.2241064],"study_design_scores_gemma":[0.0004447635,0.00003033399,0.0001151858,0.000004390738,9.775874e-7,0.000004678162,0.00004106747,0.9002642,0.03398344,0.0611953,0.003747771,0.0001678746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001990023,0.00006172076,0.9881124,0.0006341242,0.0002798379,0.0002855847,0.000002453581,0.0002250571,0.008408776],"genre_scores_gemma":[0.6744788,0.00000170168,0.3244597,0.0007238996,0.0000264363,0.00004209713,4.840432e-7,0.000005867929,0.0002610399],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8991969,"threshold_uncertainty_score":0.451634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121909854033767,"score_gpt":0.2984987307938818,"score_spread":0.2672796322535442,"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."}}