{"id":"W2078459001","doi":"10.1109/wacv.2014.6836059","title":"Improving background subtraction using Local Binary Similarity Patterns","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pixel; Background subtraction; Computer science; Artificial intelligence; Binary number; Similarity (geometry); Subtraction; Pattern recognition (psychology); Computer vision; Component (thermodynamics); Image (mathematics); Mathematics; Arithmetic","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":[0.0006943998,0.0002338892,0.000308681,0.0002377915,0.0001644555,0.0002178245,0.001000532,0.0001240475,0.0000123436],"category_scores_gemma":[0.000006724975,0.0002255722,0.0001245622,0.0003210258,0.00009528314,0.0005832504,0.0002088603,0.0002748144,0.00003776572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007045487,"about_ca_system_score_gemma":0.00006636502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009063194,"about_ca_topic_score_gemma":0.00001428408,"domain_scores_codex":[0.9979988,0.0002485305,0.0004702985,0.0006520234,0.0003518292,0.000278482],"domain_scores_gemma":[0.9978861,0.000266679,0.0003194018,0.001117503,0.000302939,0.0001073335],"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":[0.0000226168,0.0002686041,0.0005983796,0.00006738136,0.00001689091,0.000001823183,0.00009597471,0.002698401,0.01655198,0.01254076,0.00006711107,0.9670701],"study_design_scores_gemma":[0.0003531766,0.0004026294,0.009281664,0.0001522322,0.000009403745,0.00001814124,0.00001504068,0.9736031,0.01232149,0.002755477,0.0008048432,0.0002828185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07467772,0.000006466529,0.923888,0.0002905168,0.000486237,0.0002590332,0.000004645803,0.000146318,0.000241011],"genre_scores_gemma":[0.8510277,0.000005513186,0.1484826,0.0002297072,0.0002016192,0.00002053672,0.000006326805,0.00001398749,0.00001207631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9709047,"threshold_uncertainty_score":0.9198566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04789635418718716,"score_gpt":0.3338327951447669,"score_spread":0.2859364409575798,"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."}}