{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003127709,0.001180663,0.002367562,0.004086473,0.0009527099,0.001480199,0.002830751,0.001688782,0.001447802],"category_scores_gemma":[0.01267646,0.0004670704,0.001944143,0.003631107,0.001155298,0.002277131,0.002024284,0.001119862,0.001242627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006573369,"about_ca_system_score_gemma":0.001134146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400681,"about_ca_topic_score_gemma":0.002571231,"domain_scores_codex":[0.9943462,0.0008456367,0.0004029894,0.001439002,0.002532553,0.0004336581],"domain_scores_gemma":[0.9960285,0.0008244253,0.0005627751,0.001057347,0.001409814,0.0001170614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001176864,0.0002474851,0.007102197,0.0004942776,0.0004095166,0.0006735221,0.0004298112,0.07425324,0.06090821,0.01179672,0.005210563,0.8372976],"study_design_scores_gemma":[0.0001078811,0.0004489303,0.008175749,0.00006611311,0.0002335439,0.00141446,0.0003439171,0.8389072,0.1183804,0.0161739,0.01563287,0.000114969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04181703,0.0007931768,0.9544989,0.00007962186,0.0001201686,0.0001383629,0.00009474176,0.001256181,0.00120167],"genre_scores_gemma":[0.4478494,0.0007042425,0.5465208,0.000290695,0.0001223683,0.0002056496,0.001091513,0.0004302853,0.002785107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004086473,"threshold_uncertainty_score":0.01654112,"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."}}