{"id":"W2000384087","doi":"10.1145/1459359.1459562","title":"Bi-layer video segmentation with foreground and background infrared illumination","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Image segmentation; Foreground detection; Infrared; Pattern recognition (psychology); Object detection; Optics; Physics","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":[0.0007524141,0.0007844854,0.0007121381,0.0007323822,0.0003600247,0.001110273,0.00117242,0.0007858801,0.001479099],"category_scores_gemma":[0.0013692,0.0004635504,0.00072578,0.000637361,0.0004375436,0.002157717,0.0009002438,0.0008638597,0.0008016365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006305946,"about_ca_system_score_gemma":0.0005635314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002368009,"about_ca_topic_score_gemma":0.003590681,"domain_scores_codex":[0.999355,0.0001012638,0.00003383062,0.0001747026,0.0002183308,0.0001168116],"domain_scores_gemma":[0.9994443,0.0001917194,0.00006890888,0.0001267704,0.000129043,0.00003933723],"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":[0.0007069429,0.0002024919,0.002171067,0.0003132132,0.0001004422,0.0001697818,0.0002929533,0.06873199,0.4182256,0.007680121,0.000999206,0.5004061],"study_design_scores_gemma":[0.00002213047,0.0001704845,0.001727543,0.00002791276,0.00007561022,0.0002712178,0.00007985162,0.7577077,0.2334296,0.002433292,0.004020551,0.00003418205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02456379,0.0002975043,0.9727383,0.00005563402,0.0000246902,0.00003890746,0.0000279849,0.0008288792,0.001424317],"genre_scores_gemma":[0.1828537,0.0004190137,0.8130429,0.00008939343,0.00003784377,0.00004410516,0.0002689531,0.0002152341,0.003028924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002368009,"threshold_uncertainty_score":0.00494802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04761873593033814,"score_gpt":0.2899382419753267,"score_spread":0.2423195060449886,"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."}}