{"id":"W2001160538","doi":"10.1109/icdim.2009.5356792","title":"Adaptive foreground segmentation using fuzzy approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pixel; Artificial intelligence; Histogram; Pattern recognition (psychology); Image segmentation; Cluster analysis; Computer science; Computer vision; Segmentation; Fuzzy logic; Frame (networking); Fuzzy set; Mathematics; Image (mathematics)","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":[0.0003662304,0.00008331864,0.00009642249,0.00005851367,0.00009142876,0.0001182833,0.000260024,0.00003255322,0.000002785846],"category_scores_gemma":[0.000008879525,0.00006998234,0.00003996943,0.0002864787,0.00001221335,0.0005899069,0.00003042666,0.00005816813,0.000008102392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003587062,"about_ca_system_score_gemma":0.00002580241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000232998,"about_ca_topic_score_gemma":0.000001905885,"domain_scores_codex":[0.9992248,0.00007858944,0.0001173597,0.0002422563,0.0001623246,0.0001746397],"domain_scores_gemma":[0.9995905,0.00003667781,0.00004607711,0.0002449999,0.00004152264,0.00004021074],"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":[0.0000112202,0.0001311694,0.001525777,0.00000532215,0.00002192844,0.000008492886,0.0009306963,0.003045978,0.004020256,0.3053922,0.0002255389,0.6846814],"study_design_scores_gemma":[0.0007658568,0.0003123745,0.04310603,0.00001483306,0.00001044688,0.00007918203,0.0002807259,0.8112507,0.006617314,0.1368608,0.0001972456,0.0005044091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01167668,0.00004067063,0.9535803,0.000106465,0.00009973491,0.00009181328,1.963185e-7,0.0001357564,0.03426845],"genre_scores_gemma":[0.4637057,0.000001295952,0.5359069,0.0002794398,0.00003121533,0.000001018057,7.579104e-7,0.00000176404,0.00007189345],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8082048,"threshold_uncertainty_score":0.2853797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07371928966222245,"score_gpt":0.3237875592892543,"score_spread":0.2500682696270319,"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."}}