{"id":"W2164711584","doi":"10.1109/icassp.2000.859210","title":"Texture decomposition and correlation thresholding for realistic low-bit rate model-based coding","year":2002,"lang":"en","type":"article","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Bit rate; Thresholding; ENCODE; MPEG-4; Computer vision; Facial expression; Coding (social sciences); Computer facial animation; Pattern recognition (psychology); Facial Action Coding System; Animation; Computer animation; Computer graphics (images); Image (mathematics); Mathematics; Computer hardware","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":[0.0003621603,0.0003666233,0.0003631224,0.0003989257,0.0001890882,0.0005714598,0.0003897281,0.0003544492,0.002322235],"category_scores_gemma":[0.001877521,0.0002213491,0.0002748194,0.0005694876,0.0003406358,0.0007051858,0.0004170463,0.0006177995,0.0005227024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003327552,"about_ca_system_score_gemma":0.0004342557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054717,"about_ca_topic_score_gemma":0.001395772,"domain_scores_codex":[0.9997814,0.00004835871,0.00001096291,0.00002295954,0.0001167897,0.00001946379],"domain_scores_gemma":[0.9996399,0.0001331211,0.00003073484,0.0001092276,0.00007070534,0.00001631832],"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":[0.000266921,0.00007998114,0.0005355452,0.000137001,0.00002153417,0.0001753151,0.00009553265,0.1647392,0.2848728,0.04240599,0.004028937,0.5026413],"study_design_scores_gemma":[0.00001058444,0.00003301009,0.0002310467,0.000008697336,0.00000646206,0.0001413871,0.00000780362,0.9585122,0.03518537,0.003573467,0.002277532,0.00001238162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009875274,0.00008021877,0.9888483,0.00006603501,0.00002099863,0.00002213027,0.00001774588,0.0002859439,0.0007833209],"genre_scores_gemma":[0.2260059,0.0003445542,0.7711528,0.00005840015,0.00003613055,0.0001003194,0.0001605276,0.0002029161,0.001938442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002322235,"threshold_uncertainty_score":0.007768631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675145087753836,"score_gpt":0.2618639766989715,"score_spread":0.2351125258214332,"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."}}