{"id":"W2040406368","doi":"10.1109/ist.2010.5548529","title":"Using temporal correlation for fast and highdetailed video tone mapping","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tone mapping; Computer science; Frame (networking); Tone (literature); Reference frame; Computer vision; Filter (signal processing); Artificial intelligence; Range (aeronautics); Block-matching algorithm; Motion estimation; High-dynamic-range imaging; High dynamic range; Video processing; Dynamic range; Video tracking","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005053686,0.0004596598,0.0002609315,0.0004791099,0.0002274871,0.0004912845,0.000372678,0.0003288629,0.00211619],"category_scores_gemma":[0.001900288,0.0001872069,0.0002440121,0.0006239114,0.0003242936,0.0008075655,0.0006071233,0.0004348987,0.0004463511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001980735,"about_ca_system_score_gemma":0.0003991033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006933731,"about_ca_topic_score_gemma":0.00136465,"domain_scores_codex":[0.9997472,0.00005682605,0.00001150956,0.00003878387,0.0001230708,0.00002250116],"domain_scores_gemma":[0.9993142,0.0003126299,0.00007852036,0.0001269872,0.0001341099,0.00003359102],"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":[0.0003752679,0.00009033125,0.001023817,0.0001744001,0.00004301906,0.0002290872,0.000166664,0.02096261,0.3830422,0.02103603,0.002091604,0.570765],"study_design_scores_gemma":[0.00007777652,0.0003837865,0.002928158,0.0000388201,0.00006349498,0.001464033,0.00007308484,0.6558999,0.3118878,0.007560935,0.01954584,0.00007632669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01848526,0.0001556498,0.9800514,0.00003081169,0.00002832535,0.00002879129,0.00001810345,0.0002669215,0.0009346814],"genre_scores_gemma":[0.1790251,0.0003536756,0.8186768,0.00005668423,0.00005116859,0.00006873545,0.00009487147,0.00009586811,0.001577133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00211619,"threshold_uncertainty_score":0.007079422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929193116325327,"score_gpt":0.3006475142434573,"score_spread":0.271355583080204,"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."}}