{"id":"W2148197091","doi":"10.1109/crv.2007.7","title":"A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling","year":2007,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixture model; Segmentation; Computer science; Artificial intelligence; Gaussian; Computer vision; Image segmentation; Gaussian process; Bayesian probability; Gaussian network model; Pattern recognition (psychology); Flexibility (engineering); Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008952864,0.0001902749,0.0001848395,0.0001115967,0.0001873005,0.0002268707,0.000412872,0.0001264156,0.00002277409],"category_scores_gemma":[0.00001044211,0.0001579242,0.00008418364,0.0003450166,0.00001763819,0.0007073913,0.0001008946,0.0001372125,0.000004054472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000899644,"about_ca_system_score_gemma":0.00004152086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051619,"about_ca_topic_score_gemma":0.00004199131,"domain_scores_codex":[0.9984415,0.00007935322,0.0003201104,0.0004557789,0.0002772316,0.0004260774],"domain_scores_gemma":[0.9992604,0.00003730047,0.00008146631,0.0003940118,0.00006790803,0.0001589507],"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":[0.00005593141,0.000176878,0.0001155463,0.0000534491,0.00009559878,0.000053514,0.002403632,0.03248098,0.2723857,0.3320256,0.005594052,0.3545591],"study_design_scores_gemma":[0.0004181701,0.00001995611,0.000003339585,0.00001315879,0.00001080303,0.0000297085,0.00002735058,0.9685128,0.01347731,0.01704905,0.000209004,0.0002293492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01674113,0.0002788845,0.9794321,0.000320537,0.0002630231,0.0001937349,0.000001372408,0.0001538093,0.002615415],"genre_scores_gemma":[0.1076891,0.000009999833,0.8906329,0.001019976,0.0001065969,0.000003507601,0.000006405746,0.00001571263,0.0005157267],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9360318,"threshold_uncertainty_score":0.6439962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05085479795519433,"score_gpt":0.3001486095525238,"score_spread":0.2492938115973295,"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."}}