{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009894786,0.00007725621,0.000133742,0.0000992011,0.00005846374,0.00005648723,0.0002330309,0.00002078076,0.000640255],"category_scores_gemma":[0.00001983712,0.00006835226,0.0001216561,0.0001771255,0.00001637597,0.0001790728,0.0000435732,0.00002496861,0.00003762521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001326844,"about_ca_system_score_gemma":0.00001192878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002483567,"about_ca_topic_score_gemma":0.00001011246,"domain_scores_codex":[0.9993174,0.00001361997,0.0001867346,0.000187011,0.0001419034,0.0001532955],"domain_scores_gemma":[0.9996261,0.0000442424,0.00004782335,0.0001711302,0.00006819924,0.00004248296],"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":[0.00001165932,0.0004013845,0.001023935,0.0001720792,0.00008155433,0.00000446172,0.0006147173,0.0003524523,0.4667803,0.0006921424,0.004591538,0.5252737],"study_design_scores_gemma":[0.0004005154,0.00005468743,0.00004434763,0.00001429882,0.000006016749,2.289318e-7,0.00003332235,0.4242332,0.5743837,0.00005009176,0.0006796616,0.00009993667],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02007295,0.00002811154,0.9756118,0.0005424664,0.00006297996,0.000107816,0.000005760913,0.00006407757,0.003504011],"genre_scores_gemma":[0.8566889,0.000004757202,0.1420925,0.0002169203,0.00001650785,0.00001856979,0.000004166866,0.000003427684,0.0009542512],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8366159,"threshold_uncertainty_score":0.7010344,"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."}}