{"id":"W2131484521","doi":"10.1109/vipmc.2003.1220466","title":"Weighted vector median optimization","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Weighted median; Median filter; Vector optimization; Generalization; Mathematics; Filter (signal processing); Adaptive filter; Mathematical optimization; Optimization problem; Algorithm; Computer science; Artificial intelligence; Multi-swarm optimization; Computer vision; 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":[0.0001539606,0.0000535806,0.00005788629,0.00005586373,0.0000576909,0.0000990412,0.000301459,0.00002894615,0.00006565044],"category_scores_gemma":[0.0000243709,0.00004323886,0.00002400887,0.0002743652,0.00001308779,0.0003444856,0.00004705514,0.00004254659,0.00008637502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002432749,"about_ca_system_score_gemma":0.00004789617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003071851,"about_ca_topic_score_gemma":0.000002410939,"domain_scores_codex":[0.9994781,0.00003372142,0.00008446845,0.0001501519,0.000130473,0.0001230854],"domain_scores_gemma":[0.9996389,0.00002690002,0.0000185343,0.0002148453,0.00004583588,0.00005500043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001750926,0.0002077622,0.00004852052,0.00001576294,0.00003405012,0.0001855877,0.002257449,0.08636285,0.0146344,0.6917209,0.002179062,0.2023361],"study_design_scores_gemma":[0.00259102,0.0001816934,0.0005814217,0.00002921268,0.000009074581,0.00006243157,0.0000160591,0.7376296,0.16831,0.08682594,0.003253835,0.0005097021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002699852,0.00002377696,0.9849863,0.00247417,0.0002775,0.00004073809,1.058953e-7,0.0001858315,0.01174157],"genre_scores_gemma":[0.05054065,0.000004152356,0.9481395,0.0007345311,0.00005593143,0.000001851103,0.000001159791,0.0000037297,0.0005184865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6512668,"threshold_uncertainty_score":0.176323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01487618022698602,"score_gpt":0.2574829040569079,"score_spread":0.2426067238299219,"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."}}