{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001410901,0.0001094756,0.0001251814,0.00003226495,0.00006985573,0.0001162024,0.0005473294,0.0000308138,0.0000950717],"category_scores_gemma":[0.0000989548,0.0001040767,0.00004923661,0.0002398126,0.00002196302,0.0004898507,0.0001504721,0.00003595144,0.00006871807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005915997,"about_ca_system_score_gemma":0.00003347562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003830932,"about_ca_topic_score_gemma":0.000001114747,"domain_scores_codex":[0.9990112,0.00002148349,0.0002121642,0.0003467418,0.0001838899,0.0002244989],"domain_scores_gemma":[0.9994258,0.00004887196,0.00007850657,0.0002544981,0.0001213297,0.00007105123],"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":[0.00002092632,0.00009280806,0.00001221277,0.00006483608,0.000013092,0.000002922481,0.0006859443,0.00000417795,0.6868647,0.1732428,0.1013727,0.03762287],"study_design_scores_gemma":[0.0003710954,0.0004119452,0.00001051336,0.000006749925,0.00000387585,4.112252e-7,0.00001104044,0.1078997,0.8180932,0.00109792,0.07190792,0.000185541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004243975,0.00002195326,0.988241,0.004432892,0.0001340254,0.0006814518,0.000001545916,0.0008579601,0.005204845],"genre_scores_gemma":[0.1701877,0.00001042929,0.8229536,0.005550907,0.00006575429,0.0002886852,0.00001440773,0.00001079471,0.0009177156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1721449,"threshold_uncertainty_score":0.4244124,"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."}}