{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003197912,0.0005472868,0.0006922833,0.001116503,0.000454351,0.0007992933,0.001195711,0.0006720626,0.007377598],"category_scores_gemma":[0.0005583083,0.0003437931,0.0005502286,0.0009830189,0.0002049467,0.0009090487,0.0007626361,0.0004038386,0.002408006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003660476,"about_ca_system_score_gemma":0.0007965948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004059702,"about_ca_topic_score_gemma":0.007520074,"domain_scores_codex":[0.9996568,0.00003484547,0.0000194263,0.00008133136,0.0001545594,0.00005309222],"domain_scores_gemma":[0.9997874,0.00003411116,0.00001709321,0.00005574512,0.00009167059,0.00001408648],"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":[0.0004119335,0.0001201896,0.001485692,0.0001688714,0.00006369343,0.000129744,0.00008746722,0.008269822,0.1534864,0.002009901,0.007933402,0.8258328],"study_design_scores_gemma":[0.00004421534,0.0003080357,0.005935293,0.00003568116,0.00008592055,0.0006571296,0.0001401701,0.7812921,0.1877306,0.002116333,0.02158949,0.00006496835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02327225,0.0003663314,0.9702968,0.00007348601,0.000135825,0.00007512543,0.0002702571,0.003213953,0.002296025],"genre_scores_gemma":[0.2415516,0.0003645165,0.7474523,0.0001681521,0.00009195204,0.0001352114,0.001312629,0.0003297211,0.008593831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007377598,"threshold_uncertainty_score":0.0246805,"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."}}