{"id":"W2063744373","doi":"10.1109/mwscas.2011.6026420","title":"Contrast enhancement by adaptive mapping function with local information","year":2011,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Adaptive histogram equalization; Pixel; Artificial intelligence; Computer science; Computer vision; Histogram; Clipping (morphology); Contrast (vision); Histogram equalization; Image quality; Image restoration; Histogram matching; Pattern recognition (psychology); Image (mathematics); Image processing","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":[0.0002675698,0.0004197449,0.0003186262,0.0004919185,0.0001685351,0.0003345033,0.0004663579,0.0003565649,0.001032775],"category_scores_gemma":[0.0007555068,0.0001578243,0.0003315058,0.0003613559,0.0003080915,0.0008253177,0.000400929,0.0004086726,0.0003144475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001752713,"about_ca_system_score_gemma":0.0001582134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003790409,"about_ca_topic_score_gemma":0.0004210251,"domain_scores_codex":[0.9998739,0.0000240357,0.000006023486,0.00003049657,0.00004975052,0.0000157336],"domain_scores_gemma":[0.9998199,0.00006884708,0.00002752393,0.00002612497,0.00004724052,0.00001039504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003361615,0.0001590039,0.001096931,0.000166317,0.00006311332,0.0002263542,0.0001308554,0.03232815,0.4682886,0.00554442,0.000899636,0.4907604],"study_design_scores_gemma":[0.0000622272,0.0004928329,0.00305046,0.00001950088,0.00007717596,0.001401736,0.00004323872,0.6734785,0.3112878,0.003073393,0.006959953,0.00005310426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08172656,0.0005459533,0.9153341,0.00007403514,0.00003986267,0.00004046031,0.00001079749,0.000573282,0.001654828],"genre_scores_gemma":[0.577548,0.0004529109,0.4185286,0.00006873158,0.0000478109,0.00006133652,0.0000444462,0.0000747314,0.00317346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001032775,"threshold_uncertainty_score":0.003454983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339125650188883,"score_gpt":0.1828670764325989,"score_spread":0.1694758199307101,"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."}}