{"id":"W4403024210","doi":"10.1109/pacrim61180.2024.10690201","title":"Combining SIFT and BRISK Descriptors to Improve Image Matching Accuracy","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Scale-invariant feature transform; Artificial intelligence; Computer science; Matching (statistics); Image matching; Pattern recognition (psychology); Computer vision; Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002881007,0.001419619,0.001927506,0.005276741,0.00048402,0.002103282,0.001766053,0.001172877,0.003817318],"category_scores_gemma":[0.008789402,0.0006667465,0.001146898,0.005084642,0.0005807893,0.00432193,0.002060811,0.001075691,0.002861914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000913841,"about_ca_system_score_gemma":0.001027425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004099086,"about_ca_topic_score_gemma":0.006049729,"domain_scores_codex":[0.9963347,0.0002969582,0.0002271789,0.000628814,0.002186468,0.000325892],"domain_scores_gemma":[0.9962755,0.0008535939,0.0003514935,0.0009249023,0.001504253,0.00009010251],"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":[0.0007191213,0.0002992365,0.006200336,0.0004129793,0.0003434335,0.00009833859,0.00006662773,0.025163,0.07988355,0.001940065,0.006286561,0.8785868],"study_design_scores_gemma":[0.0002659758,0.001532882,0.02657937,0.0001003022,0.0006010353,0.001320087,0.0002250326,0.7460903,0.2005121,0.00586699,0.01669416,0.0002118001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1251871,0.003253018,0.8561612,0.000228797,0.0003489802,0.0003762445,0.0004577847,0.008351549,0.005635328],"genre_scores_gemma":[0.4952518,0.001235583,0.4949202,0.0002405943,0.0001845239,0.0001568662,0.001647351,0.0005479509,0.005815114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005276741,"threshold_uncertainty_score":0.01523638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292774298544648,"score_gpt":0.3000182391713118,"score_spread":0.2870904961858653,"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."}}