{"id":"W4388087356","doi":"10.2316/j.2023.206-0938","title":"TWO-STAGE FRAME MATCHING IN VSLAM BASED ON FEATURE EXTRACTION WITH ADAPTIVE THRESHOLD FOR INDOOR TEXTURE-LESS AND STRUCTURE-LESS, 1-7. SI","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Urban and spatial planning","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Artificial intelligence; Texture (cosmology); Frame (networking); Stage (stratigraphy); Extraction (chemistry); Computer vision; Computer science; Feature matching; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); Mathematics; Geology; Chemistry; Chromatography; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0001860273,0.00009501442,0.0001130781,0.000142599,0.00005634442,0.00009812941,0.00008883075,0.00005710542,0.00001258072],"category_scores_gemma":[0.00002196902,0.0000726987,0.00002276806,0.00009434695,0.00003374099,0.0003013609,0.00002324362,0.0001994331,9.954057e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009321625,"about_ca_system_score_gemma":0.00001431799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004695137,"about_ca_topic_score_gemma":0.0002112524,"domain_scores_codex":[0.9992386,0.00001751644,0.0001777107,0.0001189663,0.0003501725,0.00009703331],"domain_scores_gemma":[0.9995538,0.0001058798,0.0002261136,0.00003873208,0.0000348284,0.00004063982],"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":[0.0003004041,0.00003806916,0.04242865,0.00001071798,0.00002643912,0.00004632987,0.0005265467,0.9417596,0.004000491,0.001366479,0.0001904597,0.009305775],"study_design_scores_gemma":[0.001206288,0.0001785531,0.2321149,0.0001741629,0.00001226884,0.00002998669,0.0004477607,0.7634705,0.0003027257,0.001868937,0.00008324299,0.0001106935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9595928,0.00001484036,0.03863437,0.001176795,0.0002201463,0.0001357452,0.00002290903,0.00001435028,0.0001880672],"genre_scores_gemma":[0.9915141,0.000009079962,0.008125456,0.0001698574,0.00008434962,0.000001785958,0.00002041961,0.000009440892,0.00006553673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1896863,"threshold_uncertainty_score":0.2964567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562835580459855,"score_gpt":0.2636439724569264,"score_spread":0.2480156166523279,"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."}}