{"id":"W2913568723","doi":"10.1109/cisp-bmei.2018.8633130","title":"Foreground Segmentation in Video Sequences with a Dynamic Background","year":2018,"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":"Concordia University","funders":"","keywords":"Artificial intelligence; Pixel; Computer science; Segmentation; Computer vision; Foreground detection; Image segmentation; Pattern recognition (psychology); Background subtraction; Feature (linguistics); Hue; HSL and HSV; Ground truth","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":[0.0005500667,0.000101381,0.0001144856,0.0001088565,0.00007318558,0.0001837847,0.0003620612,0.00003303947,0.00002960571],"category_scores_gemma":[0.000009913129,0.00007311822,0.00001854043,0.0005655999,0.00009383342,0.0008583148,0.00005280499,0.00006383593,0.00005581554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006462957,"about_ca_system_score_gemma":0.00006236268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002643607,"about_ca_topic_score_gemma":0.004852084,"domain_scores_codex":[0.9990036,0.00009697918,0.000153435,0.0003218632,0.0001947546,0.0002293472],"domain_scores_gemma":[0.9994371,0.0001052,0.00005261457,0.0003032042,0.00006383361,0.00003802929],"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":[0.0001205378,0.0002388921,0.2012848,0.00007051341,0.00008789163,0.0001549833,0.005386468,0.0002659379,0.0100094,0.1263901,0.0003947561,0.6555957],"study_design_scores_gemma":[0.003491144,0.002282948,0.670225,0.0002215141,0.00001364528,0.0002827475,0.001465117,0.2105017,0.01749364,0.09051269,0.002150824,0.00135909],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.303336,0.0000223344,0.6910415,0.0003399257,0.0001208106,0.00009149298,2.438963e-7,0.00008329524,0.004964387],"genre_scores_gemma":[0.7148079,0.000004760887,0.2846631,0.0002658537,0.00002379255,0.0000105242,0.000001116958,0.000004259397,0.0002187047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6542367,"threshold_uncertainty_score":0.2981675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02877246700327192,"score_gpt":0.3218143820153359,"score_spread":0.2930419150120639,"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."}}