{"id":"W2145570744","doi":"10.1109/icip.1997.647741","title":"Multiscale segmentation and approximation for significant description of 2D contours","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Inscribed figure; Curvature; Image segmentation; Line segment; Computer science; Level set (data structures); Artificial intelligence; Constant (computer programming); Scale-space segmentation; Set (abstract data type); Geometry; Approximation algorithm; Algorithm; Computer vision; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000194353,0.00005502578,0.00008197365,0.00006370289,0.00003514092,0.00004692648,0.0001103774,0.0000296566,0.00005179218],"category_scores_gemma":[0.00005368961,0.00004863561,0.00001979269,0.00008702555,0.00004422613,0.0005607853,0.000023705,0.00002324112,0.000002518465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001876516,"about_ca_system_score_gemma":0.000003691251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001705429,"about_ca_topic_score_gemma":0.000001778494,"domain_scores_codex":[0.9993715,0.00003037838,0.0002041079,0.0001582406,0.0001517106,0.00008410463],"domain_scores_gemma":[0.9995903,0.00007026314,0.00009640959,0.0001177556,0.0000793276,0.00004599202],"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":[0.000004186908,0.000103431,0.0003145783,0.00006897588,0.00000782943,3.211941e-7,0.001357982,0.000003859777,0.3772441,0.005881924,0.005551844,0.6094609],"study_design_scores_gemma":[0.0006202582,0.0001579598,0.0007639252,0.00001515685,0.000006574898,0.000001810454,0.0002698436,0.3542916,0.6421605,0.001593059,0.00003026728,0.00008911855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008453891,0.00002873898,0.9901296,0.0002784977,0.00004918597,0.0005171339,0.00000210774,0.0001135228,0.0004273322],"genre_scores_gemma":[0.3427649,0.00001910627,0.656738,0.0001306843,0.000009093815,0.0000650401,0.000004768539,0.000002787453,0.0002656684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6093718,"threshold_uncertainty_score":0.1983303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04688936098986827,"score_gpt":0.2722644795132443,"score_spread":0.225375118523376,"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."}}