{"id":"W1570921517","doi":"10.1109/icip.2004.1421633","title":"Unsupervised motion detection using a markovian temporal model with global spatial constraints","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Maximum a posteriori estimation; Artificial intelligence; Motion estimation; Markov process; Markov random field; Pattern recognition (psychology); Segmentation; Image segmentation; Computer vision; Algorithm; Mathematics; Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001045433,0.0005871363,0.0008705088,0.0008781131,0.0003782456,0.0007526729,0.001771562,0.0008887622,0.0009295027],"category_scores_gemma":[0.003002744,0.0006282614,0.001136777,0.0006702907,0.0008343931,0.001707963,0.0007448772,0.0009808097,0.0003893267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008192468,"about_ca_system_score_gemma":0.00120159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006068907,"about_ca_topic_score_gemma":0.008698073,"domain_scores_codex":[0.9993446,0.0001673557,0.00002923419,0.0001938385,0.0002036952,0.00006125221],"domain_scores_gemma":[0.9986095,0.0007417437,0.0002237326,0.0001362367,0.0002387803,0.00004992168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002026039,0.0001088251,0.002236017,0.0001717981,0.0001588385,0.0001595493,0.0002114181,0.7263618,0.02702189,0.07563517,0.001693769,0.1660383],"study_design_scores_gemma":[0.000005204447,0.00001674103,0.0002943411,0.000004754696,0.000008996449,0.00003356405,0.000004248074,0.9906886,0.00137274,0.007060658,0.0004993716,0.00001067243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005434836,0.00005785016,0.9940345,0.00005343478,0.000006835837,0.00001190404,0.00002909133,0.0001025978,0.0002689979],"genre_scores_gemma":[0.3527055,0.0004388325,0.6408918,0.0001973227,0.0001191134,0.0002802968,0.0005699364,0.0002125596,0.004584599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006068907,"threshold_uncertainty_score":0.0120672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817542366850705,"score_gpt":0.2654308041749714,"score_spread":0.2472553805064644,"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."}}