{"id":"W2052751181","doi":"10.1109/icmew.2012.90","title":"Motion Segmentation Based on 3D Histogram and Temporal Mode Selection","year":2012,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer vision; Image segmentation; Selection (genetic algorithm); Mode (computer interface); Motion estimation; Process (computing); Motion (physics); Image (mathematics)","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":[0.0004571726,0.00006822357,0.00006180674,0.0000797516,0.00008191478,0.00005462737,0.0000646992,0.0000341858,0.00001042707],"category_scores_gemma":[0.00001602734,0.00005967897,0.00001799977,0.0001750031,0.000009901213,0.000480408,0.00001344715,0.00005334173,0.00001183849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004351893,"about_ca_system_score_gemma":0.00001071773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009381715,"about_ca_topic_score_gemma":0.00001899657,"domain_scores_codex":[0.9993594,0.0001140607,0.00008864227,0.0001535503,0.00013612,0.000148238],"domain_scores_gemma":[0.9997076,0.00005096866,0.00003886213,0.0001224144,0.0000260981,0.00005410614],"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":[0.00001284951,0.0001674381,0.3136987,0.0000148196,0.000006021846,4.152914e-7,0.0003120426,0.001806975,0.003007234,0.005445596,0.00033152,0.6751964],"study_design_scores_gemma":[0.0003603554,0.0001295602,0.1274733,0.000006583362,0.000003275977,0.000005522167,0.000008029544,0.8625696,0.007712171,0.0005484016,0.001027515,0.0001556072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0366206,0.00001628066,0.9610372,0.0001845343,0.0002528004,0.00007851541,2.623489e-7,0.0001658086,0.001644058],"genre_scores_gemma":[0.6956112,0.000001160324,0.3040268,0.0002402811,0.00004341443,0.000006018936,0.000003095009,0.000003015743,0.00006500465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8607627,"threshold_uncertainty_score":0.2433638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02802591688540488,"score_gpt":0.3104100020649499,"score_spread":0.282384085179545,"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."}}