{"id":"W1594139388","doi":"10.1155/2015/607407","title":"Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction","year":2015,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Western Economic Diversification Canada; Natural Sciences and Engineering Research Council of Canada; Grand Challenges Canada; Canada Foundation for Innovation","keywords":"Chrominance; Artificial intelligence; Computer vision; Color image; Computer science; Color space; Color balance; Sigmoid function; Color histogram; Grayscale; Pixel; Luminance; Image (mathematics); Image processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003596548,0.0004051175,0.0002254892,0.0004430035,0.0001170457,0.0004099317,0.000364563,0.0003300461,0.0009973805],"category_scores_gemma":[0.0007281965,0.0001504056,0.0005166567,0.0004582146,0.000318139,0.0005032178,0.0002877048,0.0003095663,0.0003187344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002054711,"about_ca_system_score_gemma":0.000199792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000660784,"about_ca_topic_score_gemma":0.0006017915,"domain_scores_codex":[0.9998411,0.00003239854,0.000008756064,0.00002838762,0.00007597124,0.00001346177],"domain_scores_gemma":[0.9997762,0.00008422732,0.0000306893,0.00003092917,0.00006696616,0.00001101306],"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":[0.0004736914,0.0001200339,0.002066084,0.0002713106,0.00007038988,0.000417167,0.000192535,0.0517697,0.4398575,0.005737713,0.0008609832,0.4981628],"study_design_scores_gemma":[0.00005017549,0.0004371811,0.004172385,0.00002581673,0.00006445162,0.001913661,0.00006083465,0.6953621,0.2901945,0.001630365,0.006035757,0.00005269069],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1067967,0.0007139143,0.8893672,0.00008450621,0.00004389559,0.00005171528,0.00002143043,0.0005572541,0.002363364],"genre_scores_gemma":[0.5541589,0.0009670117,0.4393797,0.00004444497,0.00002972133,0.00004451958,0.00004998316,0.00009366911,0.005232065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009973805,"threshold_uncertainty_score":0.003336608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09612988115925784,"score_gpt":0.4053410985244286,"score_spread":0.3092112173651708,"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."}}