{"id":"W2000085115","doi":"10.1109/icip.2014.7025890","title":"Streaming spatio-temporal video segmentation using Gaussian Mixture Model","year":2014,"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":"Computer science; Segmentation; Mixture model; Frame (networking); Artificial intelligence; Scalability; Gaussian; Computer vision; Similarity (geometry); Consistency (knowledge bases); Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005817522,0.0007602568,0.0008558268,0.001507914,0.0003510391,0.0008176137,0.001121601,0.0009117525,0.0008544709],"category_scores_gemma":[0.001296424,0.0004203576,0.001145999,0.001306652,0.0004113073,0.000979437,0.0006260026,0.0008129512,0.000676337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007599769,"about_ca_system_score_gemma":0.0007159243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01075095,"about_ca_topic_score_gemma":0.007470533,"domain_scores_codex":[0.9995025,0.00007763729,0.00002759944,0.0001795719,0.0001609271,0.00005158395],"domain_scores_gemma":[0.9996619,0.0000992787,0.0000440156,0.00004967849,0.0001215074,0.00002359948],"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":[0.000430528,0.0001035272,0.002095519,0.0001850761,0.0001789382,0.0003016815,0.0002436724,0.3425355,0.07842002,0.008201385,0.004517894,0.5627862],"study_design_scores_gemma":[0.000004062228,0.00001934413,0.0004595988,0.000005538615,0.00001617365,0.00006222996,0.00001533475,0.990457,0.006341566,0.00162976,0.000976472,0.00001303569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006732057,0.0002120099,0.9915615,0.00004261627,0.00002629592,0.00002603585,0.0000593509,0.0009967412,0.0003434547],"genre_scores_gemma":[0.2765408,0.0007803596,0.7180751,0.00009681147,0.00009245474,0.0001405507,0.0008828994,0.0003056632,0.003085301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01075095,"threshold_uncertainty_score":0.02137673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03320346425556132,"score_gpt":0.3051771870910915,"score_spread":0.2719737228355302,"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."}}