{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001750285,0.0007913645,0.001140542,0.001013196,0.0005508326,0.001188893,0.00108444,0.001285862,0.002545696],"category_scores_gemma":[0.00849026,0.0006664825,0.0009070344,0.001060031,0.001007188,0.001844531,0.001153678,0.001547291,0.0008408185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009862751,"about_ca_system_score_gemma":0.001340463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00912888,"about_ca_topic_score_gemma":0.005191503,"domain_scores_codex":[0.9983911,0.000487819,0.00008050393,0.0003342796,0.0006149157,0.00009137091],"domain_scores_gemma":[0.9981472,0.001027574,0.0001787042,0.0002754165,0.0003332797,0.00003779856],"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":[0.00008341735,0.00003711938,0.0006965558,0.0001875563,0.00009948659,0.00008264837,0.0001060079,0.6739463,0.003947185,0.1309152,0.002375534,0.187523],"study_design_scores_gemma":[0.000008923725,0.00002622848,0.0003592789,0.00002637935,0.00001309333,0.000042552,0.00001001193,0.9595273,0.001095535,0.03599654,0.002873454,0.00002065622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009658507,0.0002389572,0.9977393,0.00006577474,0.0000253978,0.000008236027,0.0000245485,0.00008681008,0.0008451832],"genre_scores_gemma":[0.4213754,0.003151075,0.5665362,0.000318415,0.000381169,0.0002509178,0.0006495653,0.0002586244,0.007078623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00912888,"threshold_uncertainty_score":0.01815146,"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."}}