{"id":"W2112865165","doi":"10.1109/ccece.1998.682782","title":"Supervised object-based temporal filtering for enhancement of moving facial images","year":2002,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Image warping; Face (sociological concept); Object (grammar); Set (abstract data type); Feature (linguistics); Frame (networking); Pattern recognition (psychology); Object detection","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":[0.0003852965,0.0002290542,0.0002907224,0.0003023631,0.0001174003,0.0001781539,0.0002796945,0.0002304814,0.0008343641],"category_scores_gemma":[0.0007318374,0.0001272101,0.0002925533,0.0002666842,0.0002030762,0.0004121883,0.0001480883,0.0002158337,0.0002441183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045375,"about_ca_system_score_gemma":0.0002221737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001501314,"about_ca_topic_score_gemma":0.002476271,"domain_scores_codex":[0.9998972,0.00002698228,0.000004241466,0.00001899301,0.00004143863,0.00001097991],"domain_scores_gemma":[0.9997815,0.0000851001,0.00003223174,0.00003413719,0.00005617767,0.00001088774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003768241,0.000116435,0.0009511886,0.0001526275,0.00005755372,0.000106338,0.0001025187,0.05673717,0.4118334,0.00346088,0.001347662,0.5247573],"study_design_scores_gemma":[0.00001633452,0.0001822632,0.003058165,0.000008554486,0.00003843054,0.0002481384,0.00002183917,0.8713977,0.1209826,0.001993095,0.002036523,0.00001640352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07916705,0.0003176254,0.9193047,0.00004720828,0.00002446185,0.00002420332,0.0000334067,0.000369023,0.0007124042],"genre_scores_gemma":[0.4608707,0.0004868675,0.536141,0.00004657713,0.00005328424,0.00004743241,0.0001548664,0.00006421039,0.002135088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001501314,"threshold_uncertainty_score":0.002985179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03197362726734219,"score_gpt":0.2473641386067784,"score_spread":0.2153905113394363,"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."}}