{"id":"W2054144499","doi":"10.1109/iros.2005.1545168","title":"Calibrating an active omnidirectional vision system","year":2005,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Omnidirectional antenna; Computer vision; Computer science; Calibration; Artificial intelligence; Pinhole (optics); Perspective (graphical); Triangulation; Process (computing); Omnidirectional camera; Camera resectioning; Machine vision; Mathematics; Optics; Antenna (radio)","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":[0.0001621018,0.00006708111,0.00006683354,0.00004593541,0.00007939926,0.000141807,0.0003142897,0.00003679168,0.00007839078],"category_scores_gemma":[0.00001092831,0.00005159102,0.00002463651,0.0001060052,0.00001294453,0.001532109,0.00007055279,0.00007113929,0.00008842984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006886078,"about_ca_system_score_gemma":0.00001870974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001149599,"about_ca_topic_score_gemma":0.000009847518,"domain_scores_codex":[0.9993091,0.00003887657,0.0001209088,0.0002018018,0.0002035561,0.0001258026],"domain_scores_gemma":[0.9996311,0.00002075779,0.00002643195,0.0001863429,0.00006858087,0.00006679202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008472732,0.0001283186,0.0001284746,0.000008167319,0.000007932027,0.000001317131,0.000310403,0.000009589496,0.0511464,0.6908264,0.001865889,0.2555586],"study_design_scores_gemma":[0.0001182751,0.0004376515,0.0007943215,0.00006178662,0.000002303703,0.00001182727,0.0001063465,0.3934315,0.6019554,0.0005555312,0.002315401,0.0002097542],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02241358,0.000007556004,0.6088148,0.0007685195,0.0001656865,0.0001061055,4.038309e-7,0.001464979,0.3662584],"genre_scores_gemma":[0.8828037,4.096735e-7,0.1166414,0.0001272681,0.0001069293,0.000007594834,4.997086e-7,0.000002791156,0.0003094616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8603901,"threshold_uncertainty_score":0.2103821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02510176612778426,"score_gpt":0.285360405560815,"score_spread":0.2602586394330307,"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."}}