{"id":"W4368617712","doi":"10.1016/j.vrih.2023.02.004","title":"Outliers rejection in similar image matching","year":2023,"lang":"en","type":"article","venue":"Virtual Reality & Intelligent Hardware","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Putian University; Natural Sciences and Engineering Research Council of Canada; Department of Education, Fujian Province; National Natural Science Foundation of China; Science and Technology Projects of Fujian Province","keywords":"Outlier; Artificial intelligence; Matching (statistics); Pattern recognition (psychology); Feature (linguistics); Computer science; Computer vision; Similarity (geometry); Consistency (knowledge bases); Rotation (mathematics); Point set registration; Filter (signal processing); Image (mathematics); Template matching; Mathematics; Point (geometry); Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003808951,0.000194742,0.0002144833,0.0002933128,0.00007247472,0.00007258846,0.0001402149,0.0001282245,0.0000739008],"category_scores_gemma":[0.0001236078,0.0002114955,0.00009571532,0.000694709,0.00002993053,0.0001533665,0.00003890587,0.0002544412,0.0004145901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002358511,"about_ca_system_score_gemma":0.00001801706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002215502,"about_ca_topic_score_gemma":0.000204697,"domain_scores_codex":[0.9986306,0.00006416488,0.0004120941,0.000263724,0.0002718212,0.0003576119],"domain_scores_gemma":[0.9994485,0.00008377947,0.00003237634,0.0002820778,0.00005424356,0.00009905097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001218905,0.00002100892,0.0001720782,0.00006661699,0.00002189508,0.00003365642,0.001744536,0.982819,0.002671279,0.001404349,0.00380121,0.007232154],"study_design_scores_gemma":[0.0002969005,0.00009278121,0.002876138,0.0001882551,0.00002310662,0.000004236173,0.005441085,0.9569739,0.01904594,0.002563057,0.01190504,0.0005895455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3935189,0.00006816085,0.5952868,0.0007308489,0.001770938,0.000586756,0.0001121282,0.002100567,0.005824899],"genre_scores_gemma":[0.9985012,0.0004464467,0.0001972099,0.00008052275,0.0001074361,0.00001647158,0.0002572187,0.00006213708,0.0003313624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6049823,"threshold_uncertainty_score":0.8624536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02703725092580347,"score_gpt":0.2690593955638736,"score_spread":0.2420221446380702,"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."}}