{"id":"W2107051770","doi":"10.1109/mmsp.2006.285326","title":"Fast Image/Video Contrast Enhancement Based on WTHE","year":2006,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Histogram equalization; Adaptive histogram equalization; Thresholding; Computer science; Artificial intelligence; Histogram; Balanced histogram thresholding; Weighting; Computer vision; Image (mathematics); Histogram matching; Pattern recognition (psychology); Contrast (vision); Image histogram; Contrast enhancement; Image enhancement; Process (computing); Image processing; Color image","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":[0.0002871864,0.0001911554,0.0001504338,0.0001205973,0.000100505,0.0002405936,0.0007742741,0.00004019525,0.0003950178],"category_scores_gemma":[0.00001578078,0.0001592161,0.00006651961,0.0002461282,0.00005393822,0.0004144954,0.0001175252,0.0001034252,0.0004391459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009174424,"about_ca_system_score_gemma":0.00004486934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009350354,"about_ca_topic_score_gemma":0.00001432102,"domain_scores_codex":[0.9984221,0.00005066748,0.0002629918,0.0004498507,0.0004339672,0.0003804647],"domain_scores_gemma":[0.9989578,0.00007401246,0.00007761385,0.0007490568,0.00009311624,0.00004847288],"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":[0.0000358484,0.001219348,0.0002463978,0.00003146831,0.00001765066,0.00007786963,0.0000750956,0.0001565725,0.443787,0.1845618,0.246676,0.123115],"study_design_scores_gemma":[0.0004390134,0.000218684,0.0004876969,0.00002701854,0.000002948307,0.000001323268,0.000003291328,0.06999426,0.9169945,0.00195527,0.009618922,0.0002570722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005624659,0.00001102193,0.8310785,0.001616603,0.0001415519,0.0002681235,0.000001146983,0.0006634442,0.1656572],"genre_scores_gemma":[0.6615624,0.000002066388,0.3307944,0.002542416,0.00008518316,0.00008430187,0.000005628544,0.00001256657,0.004910973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.661,"threshold_uncertainty_score":0.6492645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005608157530604221,"score_gpt":0.2285789120308587,"score_spread":0.2229707545002544,"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."}}