{"id":"W2069891133","doi":"10.1109/ciisp.2007.369176","title":"Application of Opposition-Based Reinforcement Learning in Image Segmentation","year":2007,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Image segmentation; Artificial intelligence; Segmentation; Exploit; Computer vision; Image texture; Pattern recognition (psychology); Computer security","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":[0.001606557,0.0005459529,0.0009078321,0.0004467712,0.0003333013,0.0005942984,0.001017525,0.0009092725,0.001304976],"category_scores_gemma":[0.003890151,0.0002779175,0.0004425238,0.000306746,0.00132763,0.0006468817,0.0009365265,0.000938403,0.0001954416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007464845,"about_ca_system_score_gemma":0.000690809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001681329,"about_ca_topic_score_gemma":0.00116761,"domain_scores_codex":[0.9991856,0.0004160542,0.00002989679,0.00009695064,0.0002181726,0.00005330951],"domain_scores_gemma":[0.9980863,0.001348394,0.0001728978,0.00009517122,0.0002171538,0.0000801234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001292136,0.0001215788,0.0006232925,0.0001021915,0.00006757964,0.0001567276,0.000125439,0.9026889,0.006624233,0.02298944,0.0005167844,0.06585458],"study_design_scores_gemma":[0.00001735581,0.00006767957,0.00006530448,0.000006314072,0.000005045928,0.00002932311,0.000005064827,0.9932963,0.0009230088,0.005068739,0.0005091234,0.000006750002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01399223,0.0001716524,0.98303,0.0001569818,0.00003262076,0.00005120438,0.000005269218,0.0001756288,0.002384389],"genre_scores_gemma":[0.7870074,0.0001855338,0.2099065,0.0001786974,0.00004150853,0.0001850114,0.00001977035,0.00005404571,0.002421521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001681329,"threshold_uncertainty_score":0.008496344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009708222293382411,"score_gpt":0.2675170680037101,"score_spread":0.2578088457103276,"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."}}