{"id":"W2069668822","doi":"10.1109/wacv.2009.5403099","title":"Efficient SIFT matching from keypoint descriptor properties","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Scale-invariant feature transform; Matching (statistics); Pattern recognition (psychology); Artificial intelligence; Set (abstract data type); Computer science; Modular design; Image (mathematics); Precision and recall; Feature extraction; Feature (linguistics); Mathematics; Computer vision; 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":[0.0001110437,0.0001308726,0.0001351815,0.00005516463,0.0001027392,0.0001819185,0.0005675795,0.00003673161,0.00002289802],"category_scores_gemma":[0.00003459508,0.00009335155,0.00005596278,0.0001845605,0.00002204315,0.0003121255,0.0001397867,0.0001104947,0.00007549782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003961775,"about_ca_system_score_gemma":0.00002428834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006301734,"about_ca_topic_score_gemma":0.000001513378,"domain_scores_codex":[0.9989966,0.00002558416,0.0001863624,0.0003279209,0.0002231078,0.0002404941],"domain_scores_gemma":[0.9993544,0.00002224608,0.00004342624,0.0004485514,0.00005755936,0.00007381173],"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":[0.000030021,0.0002760814,0.0000456645,0.000008040908,0.00001478385,0.00005292958,0.002378257,0.000482771,0.3987451,0.06255938,0.002287011,0.53312],"study_design_scores_gemma":[0.0002189683,0.0002363386,0.001196038,0.00007543512,0.00000430746,0.000009236908,0.00008296481,0.03590917,0.8905571,0.06663226,0.004684865,0.0003933113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05549883,0.0003614749,0.938498,0.000943849,0.0001041098,0.0001414569,6.458471e-7,0.0008826157,0.00356905],"genre_scores_gemma":[0.6999147,0.0000114487,0.2982604,0.001383876,0.00004195002,0.000003310571,4.474022e-7,0.000004276959,0.0003796243],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6444159,"threshold_uncertainty_score":0.3806766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02344944531133209,"score_gpt":0.2475321525168569,"score_spread":0.2240827072055248,"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."}}