{"id":"W2119780133","doi":"10.1109/icme.2009.5202749","title":"Interactive rotoscoping through scale-space random walks","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ontario Centres of Excellence","keywords":"Random walk; Computer science; Workflow; Scale (ratio); Spline (mechanical); Segmentation; Object (grammar); Space (punctuation); Theoretical computer science; Artificial intelligence; Algorithm; Computer vision; Mathematics; Statistics; Geography; Database","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":[0.0009215482,0.0007985712,0.0006438785,0.0008608392,0.0004312998,0.0008505155,0.001001434,0.0008947648,0.006545178],"category_scores_gemma":[0.003876301,0.0005604444,0.0006716483,0.0005763486,0.0006779381,0.001317313,0.001737167,0.0007471471,0.001536461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003224254,"about_ca_system_score_gemma":0.0003513786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001023005,"about_ca_topic_score_gemma":0.001578376,"domain_scores_codex":[0.999404,0.00017917,0.00002905065,0.0001185591,0.0002104053,0.00005883882],"domain_scores_gemma":[0.9981614,0.001129486,0.0001389104,0.0003107183,0.0001633026,0.00009629608],"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":[0.000694097,0.0001290779,0.001273686,0.0004187088,0.0000900901,0.0005993682,0.0008891896,0.1121175,0.3406533,0.02235186,0.00699165,0.5137916],"study_design_scores_gemma":[0.00008606217,0.0002213313,0.001072069,0.00004923834,0.0000334318,0.0006332539,0.0001236016,0.8469852,0.1191749,0.01056874,0.0209324,0.0001196534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01797961,0.0001611795,0.9754862,0.00008515824,0.00002977732,0.00005210278,0.00004570851,0.004253868,0.001906359],"genre_scores_gemma":[0.210821,0.0002482549,0.7840663,0.00009369252,0.00003477103,0.0001316273,0.0001196288,0.001265218,0.003219537],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006545178,"threshold_uncertainty_score":0.02189583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427657940436237,"score_gpt":0.2866440761122427,"score_spread":0.2723674967078804,"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."}}