{"id":"W1982807263","doi":"10.5539/mas.v3n11p80","title":"Moving Objects Segmentation Based on Histogram for Video Surveillance","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Background subtraction; Computer science; Histogram; Color histogram; Histogram matching; Segmentation; Feature (linguistics); Background image; Pattern recognition (psychology); Image histogram; Image segmentation; Image (mathematics); Color image; Image processing; Pixel; Image texture","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002415275,0.0002021721,0.0002149311,0.0002584198,0.000544838,0.0003779391,0.001279259,0.00004349937,0.000001853277],"category_scores_gemma":[0.0001562463,0.0001919568,0.00006968161,0.001041375,0.0001397374,0.0004135863,0.00005960797,0.0001209326,0.00001379788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764911,"about_ca_system_score_gemma":0.0002454659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005471625,"about_ca_topic_score_gemma":0.000008236151,"domain_scores_codex":[0.9974511,0.00004815716,0.0002506342,0.0009285306,0.0007259739,0.0005955821],"domain_scores_gemma":[0.9984555,0.0003096271,0.0001490717,0.0008145997,0.0001294249,0.0001417468],"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":[0.00005337563,0.0001450986,0.0004495741,0.0000168564,0.000002635179,0.000003817736,0.0006445551,0.0189635,0.3380749,0.01686733,0.0001246372,0.6246538],"study_design_scores_gemma":[0.0005860797,0.000177539,0.009176614,0.00001300574,0.000001508394,0.000001947897,0.000009515212,0.9378306,0.02993318,0.02170822,0.0002616628,0.0003001227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005535695,0.00002396908,0.9867224,0.0004876991,0.000311146,0.0004730667,0.000002440435,0.0003234704,0.006120093],"genre_scores_gemma":[0.7372722,8.777054e-7,0.2603752,0.002234158,0.00004092387,0.00004754644,0.000001959667,0.000007125375,0.00002002844],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9188671,"threshold_uncertainty_score":0.7827771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087435088660738,"score_gpt":0.2919378153553886,"score_spread":0.2710634644687812,"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."}}