{"id":"W2118932613","doi":"10.1109/cvprw.2006.134","title":"Multi-Scale Contour Extraction Based on Natural Image Statistics","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Clutter; Computer science; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Computer vision; Scale (ratio); Bayesian probability; Segmentation; Image segmentation; Probabilistic logic; Prior probability; Object (grammar); Feature extraction; Radar","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":[0.001190404,0.0004945017,0.000861645,0.002546625,0.0003455822,0.001014944,0.0007985788,0.0007864262,0.0009412357],"category_scores_gemma":[0.003785961,0.0005301628,0.0007438931,0.001459047,0.0007246031,0.001726484,0.0008662216,0.0005304067,0.0005836358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004440389,"about_ca_system_score_gemma":0.0005018421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00129301,"about_ca_topic_score_gemma":0.001880788,"domain_scores_codex":[0.9993823,0.0001050722,0.00003868786,0.0001885927,0.0002387286,0.00004659967],"domain_scores_gemma":[0.9984142,0.0006686645,0.0002678114,0.0003453091,0.0002418704,0.0000622659],"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":[0.0003193349,0.00008812919,0.003612509,0.0001799256,0.00007254939,0.0002562912,0.0002172996,0.1272035,0.1431447,0.009551037,0.001907546,0.7134471],"study_design_scores_gemma":[0.00003012549,0.00009943493,0.006305929,0.00002193476,0.00003900622,0.0003480544,0.00004411317,0.9465925,0.02907147,0.01445589,0.002952859,0.00003874946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02788914,0.0002142589,0.9703642,0.00005475006,0.000007143496,0.00004578703,0.00004989302,0.0008729529,0.0005019771],"genre_scores_gemma":[0.2243922,0.0003759056,0.7741311,0.00005357627,0.00002436819,0.00005942287,0.0003003808,0.0001773024,0.0004857366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002546625,"threshold_uncertainty_score":0.006295562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114333062836516,"score_gpt":0.3028211770634809,"score_spread":0.2916778464351157,"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."}}