{"id":"W2171529458","doi":"10.1109/icpr.2014.163","title":"A New Filter for Reducing HALO Artifacts in Tone Mapped Images","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Tone mapping; Halo; Computer science; Tone (literature); Artificial intelligence; Filter (signal processing); Computer vision; Gaussian; Pattern recognition (psychology); Focus (optics); High dynamic range; Dynamic range; Physics","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":[0.0003915346,0.0006065091,0.0004571272,0.0006629656,0.0002355583,0.0005271756,0.0006844091,0.0006420632,0.00172814],"category_scores_gemma":[0.001085197,0.0002122789,0.0005186724,0.000454807,0.000332371,0.0007803727,0.0004535059,0.0005757737,0.0006375253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002542596,"about_ca_system_score_gemma":0.0002866518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006933176,"about_ca_topic_score_gemma":0.00110613,"domain_scores_codex":[0.9997217,0.00003152196,0.0000179364,0.0000549673,0.0001492628,0.00002456661],"domain_scores_gemma":[0.9993976,0.0001728455,0.0000639007,0.00007846965,0.0002341639,0.00005313554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003276058,0.00009331734,0.000699529,0.0002920257,0.00007515303,0.0001867471,0.0001176453,0.005395997,0.6386093,0.002305333,0.001149434,0.3507479],"study_design_scores_gemma":[0.0001256249,0.00102566,0.006393054,0.00005459717,0.0002540026,0.002201436,0.00009138893,0.3437808,0.6257395,0.001484387,0.0187266,0.0001229273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03209148,0.0004120964,0.9661479,0.00006753345,0.00006227499,0.00004263301,0.0000238361,0.0006071146,0.0005451815],"genre_scores_gemma":[0.1379867,0.0005581728,0.8577993,0.0002135222,0.00008286061,0.00007835215,0.0001001833,0.0001679423,0.003012954],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00172814,"threshold_uncertainty_score":0.005781233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586364035164272,"score_gpt":0.2862669239748319,"score_spread":0.2704032836231892,"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."}}