{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015392,0.0009880493,0.0006890266,0.001194019,0.000417447,0.001237418,0.001775674,0.001383049,0.001489392],"category_scores_gemma":[0.006218546,0.0009548222,0.00040424,0.0007668797,0.0007584828,0.001851541,0.001847206,0.0006408304,0.001428334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004799672,"about_ca_system_score_gemma":0.0005104819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003883296,"about_ca_topic_score_gemma":0.0004637926,"domain_scores_codex":[0.998248,0.000373685,0.0001327293,0.0002474294,0.0008230653,0.0001752071],"domain_scores_gemma":[0.9959608,0.000841361,0.0007907901,0.0009472824,0.001321109,0.000138666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004514192,0.0000882434,0.002585733,0.0006824163,0.00007639168,0.0006260519,0.0004375339,0.1539328,0.4225265,0.0488759,0.004104119,0.365613],"study_design_scores_gemma":[0.0001292405,0.001165469,0.002176305,0.0001201925,0.00007418485,0.001705165,0.0001481073,0.5213264,0.3905353,0.01211072,0.0703191,0.0001898429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01579191,0.0003441563,0.9815323,0.00007865965,0.00005029333,0.00006296776,0.00002331125,0.0006117371,0.001504639],"genre_scores_gemma":[0.2715446,0.0002940477,0.7253226,0.00006883719,0.00004079503,0.0001455022,0.00009647764,0.0001684949,0.002318717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001775674,"threshold_uncertainty_score":0.008140147,"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."}}