{"id":"W2022346954","doi":"10.1109/icip.2006.312675","title":"Active Contour Segmentation with a Parametric Shape Prior: Link with the Shape Gradient","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Montana; Ryerson University","keywords":"Segmentation; Active contour model; Maxima and minima; Parametric statistics; A priori and a posteriori; Artificial intelligence; Scale-space segmentation; Energy minimization; Image segmentation; Computer science; Regularization (linguistics); Pattern recognition (psychology); Minification; Segmentation-based object categorization; Active shape model; Computer vision; Energy (signal processing); Representation (politics); Mathematics; Mathematical optimization; Physics; Mathematical analysis; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004525159,0.0004715201,0.0004119859,0.0003422957,0.000196587,0.0006411884,0.001667447,0.0002009376,0.0001598917],"category_scores_gemma":[0.00004257155,0.0002564563,0.0000967669,0.0008940927,0.0002478745,0.0004584502,0.00078987,0.0008762987,0.00002857342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002988221,"about_ca_system_score_gemma":0.0003695281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004479422,"about_ca_topic_score_gemma":0.0001411943,"domain_scores_codex":[0.9966312,0.000229052,0.00042892,0.001037498,0.001244079,0.0004292574],"domain_scores_gemma":[0.9974337,0.000337605,0.0005953807,0.001088305,0.0003710555,0.0001739643],"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":[0.00008328455,0.0002603707,0.0003374945,0.0001083293,0.0002319075,0.00007927292,0.001639759,0.0003184982,0.000194631,0.001420913,0.009738173,0.9855874],"study_design_scores_gemma":[0.007646225,0.005266984,0.05338574,0.001519064,0.0007518285,0.0003040674,0.001802313,0.753046,0.1596144,0.008729172,0.002772296,0.005161965],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0120125,0.00008120122,0.9770868,0.004543242,0.0001325278,0.002140542,0.00001058171,0.0007666773,0.003225947],"genre_scores_gemma":[0.1820158,0.00006332418,0.8120766,0.003246091,0.0001989381,0.001059708,0.0001072247,0.00005346641,0.001178865],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9804254,"threshold_uncertainty_score":0.9999888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950003130091738,"score_gpt":0.2756739671783308,"score_spread":0.2561739358774134,"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."}}