{"id":"W2052751181","doi":"10.1109/icmew.2012.90","title":"Motion Segmentation Based on 3D Histogram and Temporal Mode Selection","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer vision; Image segmentation; Selection (genetic algorithm); Mode (computer interface); Motion estimation; Process (computing); Motion (physics); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002572486,0.0005125722,0.0005148545,0.002172629,0.000248863,0.0005601661,0.0005863212,0.0003635722,0.001368238],"category_scores_gemma":[0.0008228131,0.0002787093,0.0005502279,0.001319752,0.0002838495,0.0007545868,0.0004411604,0.0002709843,0.0006124945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117988,"about_ca_system_score_gemma":0.0003789407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002315518,"about_ca_topic_score_gemma":0.003071377,"domain_scores_codex":[0.9996793,0.00003945647,0.00001765047,0.00007429155,0.00015166,0.00003757028],"domain_scores_gemma":[0.9996513,0.00009048967,0.00005568435,0.00003923578,0.000139278,0.00002405181],"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":[0.0002541146,0.00006601577,0.002636701,0.0001542346,0.00005349261,0.0001114964,0.00013603,0.02037254,0.2244243,0.002726177,0.001672687,0.7473922],"study_design_scores_gemma":[0.00003337005,0.0001689728,0.01279056,0.00003147795,0.00007126804,0.0009029962,0.0001183211,0.8127603,0.1610399,0.003641534,0.008342697,0.00009855053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02605735,0.0003034033,0.9711089,0.00003729838,0.00003858091,0.00005260732,0.0001047988,0.001180844,0.001116301],"genre_scores_gemma":[0.3083897,0.0006511963,0.6878818,0.00007100383,0.00007735013,0.0001183754,0.0004869088,0.0002602964,0.00206334],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002315518,"threshold_uncertainty_score":0.004604101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02802591688540488,"score_gpt":0.3104100020649499,"score_spread":0.282384085179545,"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."}}