{"id":"W2097029236","doi":"10.1109/tip.2005.864161","title":"Fingerprint registration by maximization of mutual information","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Fingerprint (computing); Artificial intelligence; Computer science; Orientation (vector space); Pattern recognition (psychology); Maximization; Image registration; Mutual information; Matching (statistics); Computer vision; Feature (linguistics); Feature extraction; Fingerprint recognition; Mathematics; Image (mathematics); 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":[0.0001899016,0.00008551667,0.00008387548,0.0003320001,0.0001747596,0.0002869706,0.0002169636,0.00006043363,0.00001451433],"category_scores_gemma":[0.000008827727,0.00009052947,0.00004024555,0.0009977031,0.00004921712,0.002211631,0.000001276652,0.00009903992,0.00002533293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000513005,"about_ca_system_score_gemma":0.00006972338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007935541,"about_ca_topic_score_gemma":0.000006649797,"domain_scores_codex":[0.9990495,0.0000271069,0.0003664303,0.0001540476,0.0002909675,0.0001119018],"domain_scores_gemma":[0.9992827,0.00002144838,0.0002269333,0.0002026228,0.0002391436,0.00002721897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002839695,0.0005371552,0.00001717643,0.0002232419,0.00001048346,7.680658e-7,0.001333586,0.003597418,0.05912381,0.00215499,0.003313812,0.9296592],"study_design_scores_gemma":[0.0005609852,0.000060044,0.0003932393,0.00005007143,0.00001535768,0.00001084095,0.00007575755,0.4442213,0.5495173,0.001255425,0.003568381,0.0002713576],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002248943,0.00003507588,0.9958023,0.0003469208,0.0001553976,0.000104085,0.00001198526,0.0001285719,0.001166677],"genre_scores_gemma":[0.9519619,0.000007979868,0.04766835,0.0000610815,0.00001028212,0.00001218716,0.00001802266,0.000003954008,0.0002562208],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.949713,"threshold_uncertainty_score":0.3691685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007991599894368791,"score_gpt":0.2233873522798351,"score_spread":0.2153957523854663,"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."}}