{"id":"W2103894593","doi":"10.1109/tpami.2009.146","title":"Designing Highly Reliable Fiducial Markers","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fiducial marker; Computer science; Artificial intelligence; Computer vision; Robustness (evolution); Augmented reality; Pose; Pattern recognition (psychology)","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":[0.0001100823,0.0001756002,0.0002214906,0.0003420145,0.0001288337,0.00006608695,0.00009036795,0.00006777569,0.0001511968],"category_scores_gemma":[0.000002220418,0.0001691252,0.0001265007,0.0007430151,0.00002102784,0.0000837086,4.024787e-7,0.0001785084,0.00002005877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003433691,"about_ca_system_score_gemma":0.000006132807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002248013,"about_ca_topic_score_gemma":0.0002033291,"domain_scores_codex":[0.9991227,0.00002336492,0.0002734147,0.0002318662,0.0001525189,0.0001961047],"domain_scores_gemma":[0.9996002,0.00004081487,0.00002819749,0.0001958701,0.00004337737,0.00009153354],"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":[0.0000085991,0.00002932364,0.00008201311,0.000007196333,0.0001466653,0.000003942731,0.00005763148,0.765591,0.001143929,0.000009921677,0.00003290529,0.2328869],"study_design_scores_gemma":[0.00006719173,0.00009762507,0.0004725663,0.00002007208,0.0003934113,0.000002549796,0.00002963153,0.822796,0.175706,0.00008176491,0.0001039657,0.0002292054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004499784,0.00008346069,0.994531,0.0001151408,0.0001598418,0.00007184166,0.0000137398,0.0001298382,0.0003953589],"genre_scores_gemma":[0.9974399,0.000476349,0.001663809,0.0002333657,0.00002496526,0.000003903195,0.00001086596,0.00001531715,0.0001315443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9929401,"threshold_uncertainty_score":0.6896724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01239260680279877,"score_gpt":0.2288069678692139,"score_spread":0.2164143610664152,"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."}}