{"id":"W2094563755","doi":"10.1109/icassp.2010.5495348","title":"Singular point detection using Discrete Hodge Helmholtz Decomposition in fingerprint images","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fingerprint (computing); Singular point of a curve; Singular value decomposition; Artificial intelligence; Mathematics; Helmholtz equation; Point (geometry); Fingerprint recognition; Matching (statistics); Ridge; Pattern recognition (psychology); Singular spectrum analysis; Computer vision; Computer science; Algorithm; Mathematical analysis; Geometry; Geography","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":[0.0004945884,0.00008739911,0.00009097606,0.000468829,0.0001144622,0.0002970367,0.0003135932,0.00007451643,0.00003218811],"category_scores_gemma":[0.00006154263,0.00008373295,0.00004750543,0.0009556555,0.00003666552,0.0005620618,0.0001336602,0.0002082683,0.00003178487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005600554,"about_ca_system_score_gemma":0.00002444716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004070914,"about_ca_topic_score_gemma":0.0002197501,"domain_scores_codex":[0.9990671,0.00005396679,0.0002167366,0.0003098889,0.0001762866,0.0001760805],"domain_scores_gemma":[0.9993877,0.00003580029,0.00006775931,0.0003798919,0.00006675971,0.00006211379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003785879,0.00008764938,0.0009589769,0.000009766622,0.000003811555,0.000007693565,0.0003228953,0.00001903618,0.9134455,0.00714941,0.00002535586,0.07796609],"study_design_scores_gemma":[0.0003985589,0.00003334355,0.1043554,0.00001523974,0.000004774752,0.00007068877,0.00003744377,0.3281954,0.558036,0.007613008,0.0009023355,0.0003377337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4077747,0.000009825601,0.5909308,0.0003020893,0.0004271622,0.00007147188,4.636452e-7,0.00007664271,0.0004069624],"genre_scores_gemma":[0.9078257,0.000002481647,0.09198593,0.000100589,0.00003046454,0.00000361089,0.000001800465,0.000004264375,0.0000451529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5000511,"threshold_uncertainty_score":0.3414531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297912452011082,"score_gpt":0.286553733945507,"score_spread":0.2735746094253962,"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."}}