{"id":"W2096589459","doi":"10.1109/nafips.2005.1548575","title":"Quality evaluation of fuzzy contrast enhancement algorithms","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Fuzzy logic; Contrast (vision); Implementation; Task (project management); Algorithm; Contrast enhancement; MATLAB; Quality (philosophy); Artificial intelligence; Machine learning; Data mining; Engineering","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":[0.002492373,0.00008909829,0.0001340121,0.00006124965,0.00003302795,0.00003470766,0.0004555549,0.00003061356,0.0002323597],"category_scores_gemma":[0.00005536251,0.00007997765,0.00004084071,0.0001666838,0.00003147993,0.0005428669,0.0001156919,0.00004685559,0.00005648769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144426,"about_ca_system_score_gemma":0.00007549521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003569027,"about_ca_topic_score_gemma":0.00001746631,"domain_scores_codex":[0.9982229,0.0001453039,0.00036147,0.000247799,0.000850943,0.0001715139],"domain_scores_gemma":[0.9989019,0.00003922155,0.0001375343,0.0004884571,0.0003995939,0.00003328748],"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":[0.000002182534,0.0001694307,0.00006094307,0.000005953783,0.00001109897,1.289852e-7,0.0002288081,0.00002019131,0.06262253,0.03765941,0.002002006,0.8972173],"study_design_scores_gemma":[0.0004441052,0.00006901116,0.001382006,0.000009620099,0.000006691457,6.390403e-7,0.00001363609,0.07402835,0.918106,0.004180575,0.001630501,0.0001288314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01280245,0.0001052841,0.9354759,0.0005260147,0.00009689636,0.0003365108,6.881174e-7,0.0001732354,0.05048295],"genre_scores_gemma":[0.6819981,0.00001055377,0.3173535,0.0002142101,0.00003756746,0.00004247178,0.00000155438,0.000002568354,0.0003395322],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8970885,"threshold_uncertainty_score":0.3261394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363996604403682,"score_gpt":0.364535038223936,"score_spread":0.3108950721798991,"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."}}