{"id":"W2137596640","doi":"10.1109/tmi.2007.901984","title":"Point-Based Rigid-Body Registration Using an Unscented Kalman Filter","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Johns Hopkins University","keywords":"Kalman filter; Computer science; Computer vision; Point (geometry); Artificial intelligence; Extended Kalman filter; Rigid body; Fast Kalman filter; Unscented transform; Physics; Mathematics; Geometry; Classical mechanics","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":[0.001671223,0.0008004866,0.001091807,0.0008320711,0.0005303617,0.001075541,0.001605453,0.001281493,0.001310382],"category_scores_gemma":[0.003975588,0.0006130753,0.001207875,0.001200891,0.0009082184,0.001731311,0.001627509,0.001049406,0.001028084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005746,"about_ca_system_score_gemma":0.001582067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005861395,"about_ca_topic_score_gemma":0.004510882,"domain_scores_codex":[0.9982809,0.0003431337,0.0001043285,0.0004818125,0.0007056331,0.00008426617],"domain_scores_gemma":[0.9988992,0.0002939012,0.0001961231,0.0002486522,0.000327722,0.00003436042],"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":[0.0002118153,0.0001185774,0.002688165,0.0002495816,0.000261291,0.0002032343,0.0004019903,0.50193,0.0409033,0.016137,0.002034602,0.4348604],"study_design_scores_gemma":[0.00001512785,0.00007807722,0.0006191983,0.00001349838,0.00002783196,0.00009125462,0.00001955596,0.9839062,0.01054654,0.001796762,0.002851994,0.00003385088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001967421,0.00003582766,0.9974089,0.00001755804,0.00001440542,0.00001432211,0.00001125005,0.0003350669,0.0001952554],"genre_scores_gemma":[0.1535389,0.0002386354,0.843931,0.00006096289,0.00003930253,0.0001901874,0.0001883668,0.0001589934,0.001653724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005861395,"threshold_uncertainty_score":0.01165456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604256381870591,"score_gpt":0.2667263843505842,"score_spread":0.2506838205318783,"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."}}