{"id":"W2039708910","doi":"10.1016/j.cviu.2010.11.021","title":"Local shape descriptor selection for object recognition in range data","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Similarity (geometry); Pattern recognition (psychology); Object (grammar); Range (aeronautics); Selection (genetic algorithm); Computer vision; Cognitive neuroscience of visual object recognition; Point cloud; Computer science; Mathematics; Shape analysis (program analysis); Geometric shape; Similitude; Image (mathematics); Geometry","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":[0.000616271,0.0003602514,0.001284375,0.001222363,0.0003335823,0.0007032362,0.0009907127,0.000484335,0.002213767],"category_scores_gemma":[0.001723425,0.0002652862,0.0006028546,0.001618903,0.0004091915,0.0007834421,0.0006850077,0.0005866768,0.001347871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003685484,"about_ca_system_score_gemma":0.0006647168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002677785,"about_ca_topic_score_gemma":0.003046201,"domain_scores_codex":[0.9995039,0.00008849135,0.00003553604,0.0000974565,0.0002158098,0.00005894828],"domain_scores_gemma":[0.9991713,0.0002612193,0.00006994856,0.0001730553,0.0002717829,0.00005270878],"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":[0.000550845,0.0001787849,0.001450258,0.0001873493,0.00006924999,0.0001190484,0.00005776234,0.03410601,0.1020785,0.0037,0.006133238,0.851369],"study_design_scores_gemma":[0.00005073493,0.0001463778,0.002711341,0.00001247528,0.0000511748,0.0001989183,0.00007241859,0.9486701,0.04119115,0.004329525,0.002531446,0.00003430472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02952112,0.0005510303,0.9677817,0.00008141319,0.00004286002,0.00004236732,0.0001749143,0.001244926,0.000559625],"genre_scores_gemma":[0.5110174,0.0007810955,0.4797572,0.0001875283,0.0001306288,0.0002073025,0.001874806,0.0004292456,0.005614951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002677785,"threshold_uncertainty_score":0.007405818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06529521957399807,"score_gpt":0.2686573316324827,"score_spread":0.2033621120584846,"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."}}