{"id":"W4387886001","doi":"10.1109/lra.2023.3326700","title":"Bayesian Filtering for Homography Estimation","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Homography; Estimation; Bayesian probability; Artificial intelligence; Bayes estimator; Computer science; Computer vision; Mathematics; Pattern recognition (psychology); Statistics; Engineering","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":[0.00008560361,0.000108044,0.0001024584,0.0002111414,0.0001020593,0.00008968413,0.00004166224,0.00004730996,0.000002268268],"category_scores_gemma":[0.00001033242,0.0001184967,0.0000423506,0.0002758956,0.00001785955,0.000122671,0.000004626944,0.00004087592,0.000009426817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001766857,"about_ca_system_score_gemma":0.000002902452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001517514,"about_ca_topic_score_gemma":0.000001170121,"domain_scores_codex":[0.9994227,0.000008183795,0.0001817815,0.0001176042,0.00009376877,0.0001760131],"domain_scores_gemma":[0.9997545,0.00005411981,0.00002796764,0.00009680789,0.00002074332,0.00004584726],"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":[8.786234e-7,0.000002603908,0.00005738979,0.0001074407,0.00001409708,0.00000108776,0.00008539864,0.9792322,0.01284199,0.0004770817,0.003178315,0.00400155],"study_design_scores_gemma":[0.0001944281,0.00001179829,0.001029994,0.00003087734,0.00001434715,0.000001552723,0.00001095996,0.9964305,0.001622598,0.0002888562,0.0002222316,0.0001418615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05380058,0.00001285108,0.9438785,0.0009554619,0.0004724314,0.0001839418,0.000009517161,0.0006570917,0.00002966344],"genre_scores_gemma":[0.960072,0.00003688933,0.03935076,0.0002378561,0.00009440346,0.00002688387,0.0001277689,0.00003967136,0.00001378124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9062714,"threshold_uncertainty_score":0.4832155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048168704102067,"score_gpt":0.2171679599217537,"score_spread":0.206686272880733,"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."}}