{"id":"W1602425644","doi":"10.1109/iscas.2004.1328891","title":"Multi-reference object pose indexing and 3-D modeling from video using volume feedback","year":2004,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Silhouette; Computer vision; Computer science; Pose; Feature (linguistics); Search engine indexing; 3D pose estimation; Video tracking; Object (grammar); Matching (statistics); Pattern recognition (psychology); Mathematics","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":[0.00003236613,0.0001339478,0.0001304144,0.00006143464,0.00006866905,0.00007516554,0.00005494705,0.00009156498,0.00002902365],"category_scores_gemma":[0.00001495182,0.0001356371,0.00002004388,0.00009603233,0.00001332439,0.0001587275,0.00002253023,0.0001114397,0.00001570074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007447971,"about_ca_system_score_gemma":0.00001622228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265053,"about_ca_topic_score_gemma":0.0001779957,"domain_scores_codex":[0.999354,0.00000811261,0.0001821654,0.0001739954,0.00009784571,0.0001839042],"domain_scores_gemma":[0.9997355,0.00001191975,0.0000144492,0.0001295731,0.00003710522,0.00007145561],"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":[0.00000207653,0.000007556582,0.0005734466,0.00001106052,0.0000118263,0.000002295682,0.0001539242,0.9708409,0.02799766,0.00006966069,0.000003903231,0.0003257325],"study_design_scores_gemma":[0.0004678628,0.000007260196,0.0004009057,0.00005580184,0.00001257698,0.000002002711,0.0001124943,0.9963183,0.002145971,0.0002833306,0.0000104467,0.0001830958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4748846,0.0001004414,0.5246221,0.000006163888,0.00006983747,0.00004235416,0.000002741738,0.0001097409,0.0001620028],"genre_scores_gemma":[0.91204,0.00003772832,0.08774827,0.00004586076,0.00004985273,8.45298e-7,0.00001500975,0.00002971037,0.00003272035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4371554,"threshold_uncertainty_score":0.5531119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04150178303517704,"score_gpt":0.2387381188832544,"score_spread":0.1972363358480773,"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."}}