{"id":"W2100312669","doi":"10.1109/cadvis.1994.284495","title":"Decoupling recognition and localization in CAD-based vision","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoupling (probability); Artificial intelligence; Computer vision; Computer science; Object (grammar); CAD; Cognitive neuroscience of visual object recognition; Projection (relational algebra); Pose; Solid modeling; Pattern recognition (psychology); Algorithm; Engineering drawing; Engineering","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":[0.0008394207,0.0006851442,0.001038529,0.001702722,0.0004579519,0.002459879,0.001377363,0.001593414,0.002195933],"category_scores_gemma":[0.002794749,0.001075353,0.0006523053,0.001453168,0.00167128,0.002792133,0.001918842,0.0009952058,0.002245412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009042929,"about_ca_system_score_gemma":0.0007429594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003619374,"about_ca_topic_score_gemma":0.003320987,"domain_scores_codex":[0.9985849,0.0002863372,0.00008093837,0.0003038811,0.0006315946,0.0001123285],"domain_scores_gemma":[0.9991068,0.0002941047,0.00004722318,0.0003317413,0.0001884774,0.00003170354],"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":[0.0001096377,0.0000790691,0.0004938719,0.0002775649,0.00004288185,0.000183023,0.0002667834,0.1045395,0.05902719,0.0809421,0.004390316,0.749648],"study_design_scores_gemma":[0.00003739757,0.00009326787,0.0007147922,0.00006560763,0.00003442431,0.0003672984,0.00009119252,0.8401707,0.04633199,0.08647376,0.02553973,0.00007977762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00364845,0.000472873,0.9916932,0.0001847169,0.00003620527,0.0000294264,0.00001979153,0.001629963,0.002285413],"genre_scores_gemma":[0.1755145,0.001268339,0.8174114,0.0003459327,0.00006992569,0.0001275389,0.0002256786,0.0002828073,0.004753931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003619374,"threshold_uncertainty_score":0.007346094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457967278785024,"score_gpt":0.275757535393265,"score_spread":0.2411778626054147,"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."}}