{"id":"W2293107716","doi":"10.1109/icip.2015.7351191","title":"Probabilistic continuous edge detection using local symmetry","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Local symmetry; Invariant (physics); Homogeneous space; Symmetry (geometry); Gaussian; Lie algebra; Lie group; Mathematics; Markov random field; Probabilistic logic; Image (mathematics); Pure mathematics; Action (physics); Artificial intelligence; Computer science; Physics; Mathematical physics; Geometry; Image segmentation; Quantum mechanics","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":[0.00155445,0.0004673568,0.0009365786,0.001354407,0.000316871,0.001143035,0.002417467,0.001438798,0.001417282],"category_scores_gemma":[0.004053995,0.0006408378,0.001245026,0.0009544157,0.001552101,0.002498366,0.001571661,0.001203292,0.000540961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007441893,"about_ca_system_score_gemma":0.0006250413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001347334,"about_ca_topic_score_gemma":0.001094766,"domain_scores_codex":[0.9992009,0.0002064739,0.00002986858,0.0001906238,0.000298782,0.00007336099],"domain_scores_gemma":[0.9987223,0.0006392193,0.0002119218,0.0002203397,0.0001363687,0.00006994313],"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":[0.0002485048,0.0001165802,0.001678785,0.0001385769,0.0001146483,0.0003446103,0.0001351138,0.6756883,0.02635075,0.1839946,0.001874224,0.1093153],"study_design_scores_gemma":[0.000006129219,0.00002048026,0.0001293114,0.000003500737,0.000004468442,0.00005620464,0.000003080287,0.9714146,0.00161662,0.02644671,0.0002884853,0.00001038397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009682675,0.00006103852,0.9894983,0.00009566483,0.00001013018,0.000013719,0.00003306763,0.0002013256,0.0004041156],"genre_scores_gemma":[0.6073918,0.000305501,0.3883003,0.0001905783,0.00009190892,0.0001663782,0.0002659715,0.0002131083,0.003074369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002417467,"threshold_uncertainty_score":0.008220792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04200186255399984,"score_gpt":0.2991407051925157,"score_spread":0.2571388426385158,"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."}}