{"id":"W2059657882","doi":"10.1142/s0219467806002379","title":"REINFORCED CONTRAST ADAPTATION","year":2006,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Contrast (vision); Computer science; Artificial intelligence; Observer (physics); Image (mathematics); Reinforcement learning; Histogram; Computer vision; Ideal (ethics); Adaptation (eye); Transformation (genetics); Point (geometry); Algorithm; Mathematics","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":[0.0005377331,0.0004624065,0.0003627092,0.0002310436,0.0001753203,0.000467656,0.0008721311,0.0005661767,0.00305496],"category_scores_gemma":[0.002827269,0.0001996642,0.0003050547,0.0001368359,0.0003963267,0.0006170063,0.0007812764,0.0006721834,0.0006985056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003722122,"about_ca_system_score_gemma":0.0002644415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008601836,"about_ca_topic_score_gemma":0.0011114,"domain_scores_codex":[0.9996643,0.00007263402,0.00001277246,0.00008503261,0.0001296492,0.00003566837],"domain_scores_gemma":[0.9990743,0.0003853337,0.00009509306,0.000154602,0.0002507723,0.00003981827],"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":[0.0004039634,0.0004604768,0.002558557,0.000192202,0.0001473033,0.0002945804,0.0001804335,0.2355269,0.1752981,0.0181949,0.003259879,0.5634828],"study_design_scores_gemma":[0.00005581707,0.0002381057,0.00167854,0.00001488258,0.00003228522,0.0002394574,0.00001739866,0.9476893,0.03757375,0.006432208,0.005999167,0.00002910244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04816072,0.0001988276,0.9413643,0.0001209685,0.00008695342,0.0001112554,0.00002661704,0.001065983,0.008864407],"genre_scores_gemma":[0.8113232,0.0001550826,0.1812156,0.0001609749,0.00003620939,0.0001317824,0.00004084026,0.0001040383,0.006832371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00305496,"threshold_uncertainty_score":0.01021981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00847445703570523,"score_gpt":0.2639550271641977,"score_spread":0.2554805701284925,"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."}}