{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002020691,0.0001094496,0.0001168922,0.0001526406,0.0000907283,0.0001468548,0.00007833933,0.00007856848,0.00002414],"category_scores_gemma":[0.00001595508,0.0001092903,0.0000193158,0.0001387925,0.00003252024,0.0003751086,0.00003494162,0.0001447417,0.000003473247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007473115,"about_ca_system_score_gemma":0.000008196253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006355136,"about_ca_topic_score_gemma":0.00009012054,"domain_scores_codex":[0.9993643,0.00001568426,0.0001656906,0.0002191075,0.00007394935,0.0001613223],"domain_scores_gemma":[0.9997064,0.00006780739,0.00001908686,0.0001259007,0.00002843286,0.00005241035],"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":[0.0003688637,0.0002546379,0.001694783,0.001022288,0.0001013915,0.00002487779,0.001396341,0.02688077,0.2200627,0.005487693,0.03305506,0.7096506],"study_design_scores_gemma":[0.0007238985,0.00006670588,0.0001918607,0.00005695466,0.000008226221,0.00000941398,0.00008619531,0.9964815,0.0007123024,0.001041701,0.0004773722,0.0001438423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03087031,0.00002686879,0.9680747,0.00005019687,0.000550091,0.0001958432,0.00001300239,0.0001067281,0.0001122383],"genre_scores_gemma":[0.9507433,0.00004077045,0.04883271,0.00006262308,0.0001385482,0.00000311177,0.0001492766,0.00002559775,0.000004102335],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9696007,"threshold_uncertainty_score":0.4456729,"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."}}