{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002956548,0.0001118522,0.0001162502,0.00009623819,0.0001853983,0.0001226247,0.0001664069,0.0000410076,0.00001570161],"category_scores_gemma":[0.00001486227,0.00008525709,0.00001890502,0.000326235,0.00007372411,0.00116723,0.00005594758,0.00006134101,0.00001453121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000279931,"about_ca_system_score_gemma":0.00003893532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003837479,"about_ca_topic_score_gemma":0.00004419627,"domain_scores_codex":[0.9990836,0.00007486489,0.0001431805,0.0002958916,0.0002325922,0.000169846],"domain_scores_gemma":[0.999398,0.0001257311,0.00006455764,0.0002600092,0.00009321814,0.00005848291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001416255,0.0003472349,0.2803576,0.0001309199,0.0002168256,0.000260986,0.01284546,0.0002911554,0.01228761,0.1209398,0.005134833,0.567046],"study_design_scores_gemma":[0.003124209,0.0007250619,0.9237708,0.0000482374,0.00001794782,0.0008339508,0.0006787967,0.02870001,0.02407824,0.01186628,0.005278176,0.0008782829],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3552031,0.00004045813,0.6393902,0.0001698694,0.00006918998,0.00009735436,3.395058e-7,0.0001009768,0.004928499],"genre_scores_gemma":[0.7214188,0.00003328806,0.277095,0.0002971563,0.00002982253,0.00001175024,0.000003548147,0.000006620854,0.001103984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6434132,"threshold_uncertainty_score":0.3476683,"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."}}