{"id":"W1981875488","doi":"10.1109/iccvw.2009.5457719","title":"Valence Normalized Spatial Median for skeletonization and matching","year":2009,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletonization; Computer science; Normalization (sociology); Pattern recognition (psychology); Coding (social sciences); Artificial intelligence; Robustness (evolution); Segmentation; Curvature; Mathematics; Computer vision; Algorithm; Geometry","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":[0.00005428454,0.00004911165,0.00006678794,0.00003537636,0.00003465073,0.00002121197,0.00002793129,0.00002456347,0.00002011127],"category_scores_gemma":[0.000008624263,0.00004502643,0.00002070482,0.00004367265,0.000003036267,0.00005722491,0.000002324517,0.00002406944,0.000002664298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006247511,"about_ca_system_score_gemma":0.00000222221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000293772,"about_ca_topic_score_gemma":0.00003276943,"domain_scores_codex":[0.9997184,0.000002506215,0.00008782834,0.00006287057,0.00004368876,0.00008468986],"domain_scores_gemma":[0.9998884,0.0000132023,0.000006961508,0.00004568393,0.00001497046,0.00003082644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000982522,0.00001811573,0.0004850475,0.0001046653,0.00005074581,0.000001659388,0.001179938,0.5839606,0.01278623,0.001188309,0.0008052139,0.3994097],"study_design_scores_gemma":[0.0001489522,0.00001002034,0.0001715426,0.000008077663,0.00001388193,6.608402e-7,0.00001858628,0.9964314,0.001259641,0.00175105,0.0001163136,0.0000699259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07004959,0.00006447165,0.9287754,0.000185584,0.00003459079,0.00003751442,0.00000197478,0.000144258,0.0007066195],"genre_scores_gemma":[0.9861341,0.00005874672,0.013543,0.00007377071,0.000060479,0.00000212932,0.00001292162,0.000005951155,0.0001089623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9160845,"threshold_uncertainty_score":0.1836124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005148925794858323,"score_gpt":0.2044124495768277,"score_spread":0.1992635237819694,"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."}}