{"id":"W2110457125","doi":"10.1007/s11263-009-0276-3","title":"Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Artificial intelligence; Embedding; Computer science; Cognitive neuroscience of visual object recognition; Benchmark (surveying); Feature vector; Feature (linguistics); Outlier; Maxima and minima; Range (aeronautics); Object (grammar); Computer vision; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002561346,0.000110942,0.0001721539,0.0002822606,0.00001953481,0.00009532076,0.0001617259,0.00005593083,0.00003205665],"category_scores_gemma":[0.0000138409,0.0001088536,0.00007869277,0.0001147404,0.000008888092,0.0003943169,0.00001458728,0.0001419271,0.00001326749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710506,"about_ca_system_score_gemma":0.00001825371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009839715,"about_ca_topic_score_gemma":9.293537e-7,"domain_scores_codex":[0.9989529,0.00004651937,0.0004508107,0.00008836488,0.0003427755,0.0001186466],"domain_scores_gemma":[0.9994302,0.00005597344,0.0001351823,0.00006022118,0.0002701591,0.0000481951],"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":[0.00005441916,0.00004560742,0.0001519893,0.000004387413,0.0000211177,0.0001271606,0.0001035203,0.9366159,0.04366792,0.000009249373,0.000425934,0.01877275],"study_design_scores_gemma":[0.000696539,0.000119577,0.004725318,0.000386391,0.000009025778,0.0001385462,0.000006567441,0.9897311,0.003588532,0.0004210172,0.00005734675,0.0001199776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420335,0.00004389104,0.2567801,0.0001264803,0.0007625083,0.00004478984,0.000003325837,0.00002835067,0.0001770323],"genre_scores_gemma":[0.9301792,0.0001326465,0.06919498,0.00005267857,0.0004066979,6.940426e-8,0.00001167538,0.00001500098,0.000007011748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1881458,"threshold_uncertainty_score":0.4438921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910360717271248,"score_gpt":0.2913059001392245,"score_spread":0.272202292966512,"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."}}