{"id":"W2136545936","doi":"10.1109/mlsp.2005.1532899","title":"Video Object Segmentation and Tracking Using Probabilistic Fuzzy C-Means","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Initialization; Video tracking; Probabilistic logic; Pattern recognition (psychology); Cluster analysis; Segmentation; Image segmentation; Motion estimation; Scale-space segmentation; Object (grammar)","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":[0.001218398,0.0006878437,0.000919065,0.001674034,0.0006931809,0.0009672858,0.001587553,0.001246707,0.0008239689],"category_scores_gemma":[0.002839742,0.0005933894,0.0009482548,0.001260964,0.0008905631,0.001543586,0.0007821803,0.0008833206,0.0004220749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175144,"about_ca_system_score_gemma":0.001387575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01063752,"about_ca_topic_score_gemma":0.008750507,"domain_scores_codex":[0.9989892,0.0001668573,0.00005040238,0.0002849287,0.0004461809,0.0000623866],"domain_scores_gemma":[0.9991416,0.0003304717,0.0001078176,0.0001337148,0.0002595006,0.00002685741],"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":[0.000150821,0.00007082547,0.001025842,0.0001545151,0.0001229331,0.00009014506,0.0001590326,0.4117989,0.03864195,0.01782436,0.002118769,0.5278419],"study_design_scores_gemma":[0.000007080846,0.00002329156,0.000436673,0.000009343081,0.00001156604,0.00004802245,0.000008746963,0.9864334,0.006412248,0.005391925,0.001191444,0.00002624551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002682573,0.00007649096,0.9966006,0.00002565341,0.000008096692,0.0000207954,0.00001322457,0.0002931787,0.0002794288],"genre_scores_gemma":[0.1058094,0.0002404358,0.8926636,0.00007195279,0.00003198384,0.0001081208,0.0001218116,0.00008005019,0.000872561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01063752,"threshold_uncertainty_score":0.02115119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03238583496351655,"score_gpt":0.2956303207025642,"score_spread":0.2632444857390476,"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."}}