{"id":"W1992491299","doi":"10.1109/icassp.2013.6637889","title":"Singular point detection based on orientation filed regularization and poincaré index in fingerprint images","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Orientation (vector space); Fingerprint (computing); Regularization (linguistics); Pattern recognition (psychology); Singular point of a curve; Fingerprint recognition; Computer vision; Computer science; Mathematics; Spurious relationship; Helmholtz free energy; Geometry; Physics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003741672,0.0001017013,0.00009551844,0.0006822707,0.00009307803,0.0003553302,0.0001672981,0.00008189374,0.0001128077],"category_scores_gemma":[0.0001948084,0.00009654889,0.00002579136,0.001252446,0.00002951787,0.0005756451,0.00005758116,0.000101026,0.00006486608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008730996,"about_ca_system_score_gemma":0.0000272934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004100892,"about_ca_topic_score_gemma":0.00006211963,"domain_scores_codex":[0.9988642,0.000106994,0.0002423978,0.0003759047,0.0002635501,0.0001469248],"domain_scores_gemma":[0.9992677,0.0000786554,0.00008943224,0.0003612005,0.0001401625,0.00006283641],"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":[0.00005264449,0.000894515,0.01969594,0.0001159173,0.00002592445,0.000006097717,0.003284318,0.001084676,0.1015505,0.03073485,0.001564584,0.84099],"study_design_scores_gemma":[0.0006805832,0.00005252318,0.4206558,0.0000186154,0.000002832077,0.000003204038,0.00005450737,0.5475341,0.02383189,0.006496996,0.0004615007,0.0002074863],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1367799,0.00000788263,0.8609638,0.001095029,0.0001617131,0.0002707232,5.990665e-7,0.0001045181,0.0006159029],"genre_scores_gemma":[0.9752383,0.000003934904,0.02394514,0.0003736391,0.00001389951,0.00003193068,0.00001430612,0.000005410051,0.0003734749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8407825,"threshold_uncertainty_score":0.393715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009310290878172357,"score_gpt":0.225581032343445,"score_spread":0.2162707414652727,"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."}}