{"id":"W1535852552","doi":"10.1109/iai.2002.999926","title":"Spatio-temporal motion segmentation via level set partial differential equations","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Artificial intelligence; Image segmentation; Computer vision; Scale-space segmentation; Partial differential equation; Segmentation-based object categorization; Computer science; Segmentation; Motion field; Motion analysis; Pattern recognition (psychology); Image (mathematics); Motion (physics); Discretization; Level set method; Mathematics; Mathematical analysis","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":[0.0003275918,0.0003562818,0.000427367,0.0004721923,0.0002874956,0.0006726229,0.0006251265,0.000634308,0.0009692227],"category_scores_gemma":[0.001209279,0.0003439896,0.0004572368,0.0004590157,0.0005764049,0.0008569101,0.000559159,0.0005730352,0.0002291444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006478272,"about_ca_system_score_gemma":0.0006893107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002343603,"about_ca_topic_score_gemma":0.001801796,"domain_scores_codex":[0.9998525,0.00003285048,0.000009696388,0.00002416705,0.00006991893,0.00001084743],"domain_scores_gemma":[0.999731,0.0001429516,0.00004514364,0.00002663383,0.0000390512,0.00001509196],"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":[0.00005724002,0.00002340787,0.0005254185,0.0000905885,0.00002598123,0.00009812698,0.0001674362,0.8310017,0.05373041,0.03876476,0.0006776712,0.07483715],"study_design_scores_gemma":[0.00000247971,0.000005675323,0.00005509167,0.000002401424,0.000001765669,0.00001407303,0.000003758916,0.9942572,0.002337761,0.002838518,0.0004783565,0.000002990537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008537653,0.00007342915,0.9907669,0.00006417951,0.000009901938,0.00001461552,0.00001715371,0.0001445435,0.0003716477],"genre_scores_gemma":[0.2656917,0.0003481431,0.7317109,0.00005193289,0.00002165475,0.0001265014,0.0001283177,0.00009509855,0.001825922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002343603,"threshold_uncertainty_score":0.004700363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03361696307779714,"score_gpt":0.2764690517139383,"score_spread":0.2428520886361411,"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."}}