{"id":"W2033078764","doi":"10.1049/iet-ipr.2012.0512","title":"Greedy framework for optical flow tracking of myocardium contours","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Development Fund; Ministry of Scientific Research, Egypt; York University","keywords":"Optical flow; Computer science; Flow (mathematics); Tracking (education); Computer vision; Artificial intelligence; Greedy algorithm; Algorithm; Mathematics; Image (mathematics); Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007573917,0.0001377478,0.0002597542,0.00008167288,0.0001065641,0.0002720072,0.000630893,0.00009816067,0.000009954116],"category_scores_gemma":[0.001173215,0.0001282596,0.00009004243,0.0002373102,0.0001664345,0.0008882933,0.0001069768,0.0001805875,0.00000516819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002351357,"about_ca_system_score_gemma":0.00007449419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002142142,"about_ca_topic_score_gemma":2.67805e-7,"domain_scores_codex":[0.9985869,0.00004271038,0.0003559196,0.0003410367,0.0003730074,0.0003004419],"domain_scores_gemma":[0.998703,0.0003399605,0.0001846861,0.0003251803,0.0003258095,0.0001213508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006887541,0.00004391252,0.00004570514,0.0001857341,0.000007224552,0.000002031325,0.0004635174,0.000005368244,0.01789005,0.00437818,0.0003485346,0.9766229],"study_design_scores_gemma":[0.0006148833,0.0001991321,0.0003913212,0.0005082292,0.00002908949,0.00001614314,0.00008371069,0.1982584,0.7231723,0.07613992,0.0002492665,0.0003376288],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003812966,0.0001105811,0.9972282,0.0007424741,0.0001596599,0.0002209248,0.000001850998,0.0002932846,0.0008617512],"genre_scores_gemma":[0.2438195,0.000003013769,0.7554084,0.0005742575,0.000131173,0.00003229635,0.000001988066,0.00001378705,0.0000155884],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9762852,"threshold_uncertainty_score":0.5230274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119212198770345,"score_gpt":0.3200401852334568,"score_spread":0.2988480632457534,"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."}}