{"id":"W3034660707","doi":"10.1109/icmew46912.2020.9106053","title":"Color Balanced Histogram Equalization for Image Enhancement","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Histogram equalization; Computer science; Artificial intelligence; Computer vision; Histogram; Object detection; Visibility; Detector; Color histogram; Color normalization; Merge (version control); Image (mathematics); Pattern recognition (psychology); Color image; 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":[0.0002629727,0.0004555722,0.0002835239,0.0007168353,0.0001786654,0.0004331366,0.0004883775,0.000369671,0.002923593],"category_scores_gemma":[0.0005877432,0.0001561328,0.0002750343,0.0006176393,0.0002680059,0.0006110391,0.0004521296,0.0005101431,0.00121861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003348202,"about_ca_system_score_gemma":0.0002890682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00131728,"about_ca_topic_score_gemma":0.002072027,"domain_scores_codex":[0.999805,0.00001829915,0.000007295956,0.00005193611,0.00009258322,0.00002493291],"domain_scores_gemma":[0.9998023,0.00005247296,0.00002148067,0.00004268952,0.00007009949,0.00001103234],"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":[0.0002358053,0.0001119447,0.0006626044,0.0002135808,0.0000556235,0.00007413994,0.00004205662,0.01962939,0.3515882,0.003887933,0.00553854,0.6179603],"study_design_scores_gemma":[0.00003293236,0.0002041499,0.004074745,0.00003144514,0.00005776211,0.000571508,0.00004436231,0.4289571,0.5298256,0.005946272,0.03020663,0.00004752804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03224039,0.00236471,0.9575265,0.000173903,0.0001297791,0.0001083801,0.0001987755,0.001766916,0.005490711],"genre_scores_gemma":[0.4058744,0.002348447,0.5788395,0.0002663237,0.0001110613,0.00009022638,0.0007953473,0.0002405295,0.0114343],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002923593,"threshold_uncertainty_score":0.009780347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02647162212604157,"score_gpt":0.2851985325404298,"score_spread":0.2587269104143882,"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."}}