{"id":"W2162335326","doi":"10.1109/ccece.2011.6030413","title":"Interactive video GrowCut: A semi-automated video object segmentation framework using cellular automata","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Segmentation; Computer vision; Artificial intelligence; Image segmentation; Object (grammar); Video tracking; Segmentation-based object categorization; Scale-space segmentation; Extension (predicate logic)","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":[0.0005730055,0.0008400244,0.0007999627,0.0009228161,0.000616343,0.001302859,0.002034492,0.001304758,0.002689004],"category_scores_gemma":[0.001446456,0.0005014505,0.0009775278,0.0005791335,0.0008568248,0.001335157,0.001152696,0.0009162866,0.0007901369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020499,"about_ca_system_score_gemma":0.0009071511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00688368,"about_ca_topic_score_gemma":0.008138758,"domain_scores_codex":[0.9995142,0.00009541996,0.00002443129,0.0001366615,0.0001835667,0.00004561973],"domain_scores_gemma":[0.9994227,0.0002540585,0.00004526471,0.0001019251,0.0001335313,0.00004252182],"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":[0.0002361996,0.00008196845,0.0008450583,0.0002788768,0.0001092786,0.0004464427,0.0004427409,0.4602085,0.1042601,0.07573447,0.006324877,0.3510315],"study_design_scores_gemma":[0.000008452294,0.00003029603,0.0001446959,0.00001013351,0.000008286473,0.0001076216,0.00002407573,0.9716225,0.01299858,0.009440518,0.005584855,0.00001992561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002679488,0.00005659644,0.9956241,0.00002939951,0.0000135782,0.00002725469,0.0000415351,0.0009992934,0.0005286713],"genre_scores_gemma":[0.106031,0.000133694,0.8907793,0.00007095262,0.0000305676,0.0001259668,0.0002399459,0.0003923628,0.002196311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00688368,"threshold_uncertainty_score":0.01368725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03533057516602746,"score_gpt":0.3129165363703508,"score_spread":0.2775859612043233,"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."}}