{"id":"W1951821760","doi":"10.1109/im.1999.805353","title":"Efficient and reliable template set matching for 3D object recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Tree traversal; Template; Computer science; Voxel; Template matching; Artificial intelligence; Tree (set theory); Binary tree; Pattern recognition (psychology); Matching (statistics); Set (abstract data type); Computer vision; Algorithm; Image (mathematics); Mathematics; Combinatorics","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":[0.0001057893,0.00006335906,0.00006466669,0.00003663412,0.00005450442,0.00002882253,0.00001209007,0.00003784598,0.000033427],"category_scores_gemma":[0.00001565262,0.00006019189,0.00001449561,0.00005560509,0.000004419572,0.00001992886,0.000002102948,0.00003114287,0.00001187972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001664731,"about_ca_system_score_gemma":0.000003853092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001041303,"about_ca_topic_score_gemma":0.000005104002,"domain_scores_codex":[0.9996509,0.000006187858,0.00009922862,0.0000848138,0.00004275453,0.0001161352],"domain_scores_gemma":[0.9998503,0.00003331749,0.000009059411,0.00005477748,0.00002279681,0.00002980304],"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":[0.000002652612,0.000005193939,0.000027111,0.00005890348,0.000006714738,4.827961e-7,0.00008220252,0.9962506,0.001178732,0.000728545,0.0006311511,0.00102768],"study_design_scores_gemma":[0.000288679,0.00002224488,0.0000283275,0.0000226836,0.00001039208,0.000004946628,0.00006728988,0.9900176,0.005773206,0.001139007,0.002506251,0.0001194016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.249304,0.00005471347,0.7426952,0.00001266675,0.0001754392,0.0001812108,0.000007649319,0.0001371907,0.007431959],"genre_scores_gemma":[0.9353781,0.00002624783,0.06425937,0.00005638017,0.00001902364,0.00001095978,0.00003469236,0.00002510181,0.0001900987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6860741,"threshold_uncertainty_score":0.2454554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813699183643608,"score_gpt":0.2178491558886928,"score_spread":0.1997121640522567,"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."}}